Seasonal time series with trends are the most common data sets used in forecasting. This work focuses on the automatic processing of a
non-pre-processed time series by studying the efficiency of recurrent neural networks (RNN), in particular both long short-term memory (LSTM), and bidirectional long short-term memory (Bi-LSTM) extensions, for modelling seasonal time series with trend. For this purpose, we are interested in the learning stability of the established systems using the mean average percentage error (MAPE) as a measure. Both simulated and real data were examined, and we have found a positive correlation between the signal period and the system input vector length for stable and relatively efficient learning. We also examined the white noise impact on the learning performance.
System for Prediction of Non Stationary Time Series based on the Wavelet Radi...IJECEIAES
This paper proposes and examines the performance of a hybrid model called the wavelet radial bases function neural networks (WRBFNN). The model will be compared its performance with the wavelet feed forward neural networks (WFFN model by developing a prediction or forecasting system that considers two types of input formats: input9 and input17, and also considers 4 types of non-stationary time series data. The MODWT transform is used to generate wavelet and smooth coefficients, in which several elements of both coefficients are chosen in a particular way to serve as inputs to the NN model in both RBFNN and FFNN models. The performance of both WRBFNN and WFFNN models is evaluated by using MAPE and MSE value indicators, while the computation process of the two models is compared using two indicators, many epoch, and length of training. In stationary benchmark data, all models have a performance with very high accuracy. The WRBFNN9 model is the most superior model in nonstationary data containing linear trend elements, while the WFFNN17 model performs best on non-stationary data with the non-linear trend and seasonal elements. In terms of speed in computing, the WRBFNN model is superior with a much smaller number of epochs and much shorter training time.
Multi-task learning using non-linear autoregressive models and recurrent neur...IJECEIAES
Tide level forecasting plays an important role in environmental management and development. Current tide level forecasting methods are usually implemented for solving single task problems, that is, a model built based on the tide level data at an individual location is only used to forecast tide level of the same location but is not used for tide forecasting at another location. This study proposes a new method for tide level prediction at multiple locations simultaneously. The method combines nonlinear autoregressive moving average with exogenous inputs (NARMAX) model and recurrent neural networks (RNNs), and incorporates them into a multi-task learning (MTL) framework. Experiments are designed and performed to compare single task learning (STL) and MTL with and without using non-linear autoregressive models. Three different RNN variants, namely, long short- term memory (LSTM), gated recurrent unit (GRU) and bidirectional LSTM (BiLSTM) are employed together with non-linear autoregressive models. A case study on tide level forecasting at many different geographical locations (5 to 11 locations) is conducted. Experimental results demonstrate that the proposed architectures outperform the classical single-task prediction methods.
A Time Series ANN Approach for Weather Forecastingijctcm
Weather forecasting is most challenging problem around the world. There are various reason because of its experimented values in meteorology, but it is also a typical unbiased time series forecasting problem in scientific research. A lots of methods proposed by various scientists. The motive behind research is to predict more accurate. This paper contribute the same using artificial neural network (ANN) and simulated in MATLAB to predict two important weather parameters i.e. maximum and minimum temperature. The model has been trained using past 60 years of real data collected from(1901-1960) and tested over 40 years to forecast maximum and minimum temperature. The results based on mean square error function (MSE) confirm, this model which is based on multilayer perceptron has the potential to successful application to weather forecasting
Time Series Forecasting Using Novel Feature Extraction Algorithm and Multilay...Editor IJCATR
Time series forecasting is important because it can often provide the foundation for decision making in a large variety of fields. A tree-ensemble method, referred to as time series forest (TSF), is proposed for time series classification. The approach is based on the concept of data series envelopes and essential attributes generated by a multilayer neural network... These claims are further investigated by applying statistical tests. With the results presented in this article and results from related investigations that are considered as well, we want to support practitioners or scholars in answering the following question: Which measure should be looked at first if accuracy is the most important criterion, if an application is time-critical, or if a compromise is needed? In this paper demonstrated feature extraction by novel method can improvement in time series data forecasting process
Forecasting Electric Energy Demand using a predictor model based on Liquid St...Waqas Tariq
Electricity demand forecasts are required by companies who need to predict their customers’ demand, and by those wishing to trade electricity as a commodity on financial markets. It is hard to find the right prediction method for a given application if not a prediction expert. Recent works show that Liquid State Machines (LSM’s) can be applied to the prediction of time series. The main advantage of the LSM is that it projects the input data in a high-dimensional dynamical space and therefore simple learning methods can be used to train the readout. In this paper we present an experimental investigation of an approach for the computation of time series prediction by employing Liquid State Machines (LSM) in the modeling of a predictor in a case study for short-term and long-term electricity demand forecasting. Results of this investigation are promising, considering the error to stop training the readout, the number of iterations of training of the readout and that no strategy of seasonal adjustment or preprocessing of data was achieved to extract non-correlated data out of the time series.
Forecasting Electric Energy Demand using a predictor model based on Liquid St...Waqas Tariq
Electricity demand forecasts are required by companies who need to predict their customers’ demand, and by those wishing to trade electricity as a commodity on financial markets. It is hard to find the right prediction method for a given application if not a prediction expert. Recent works show that Liquid State Machines (LSM’s) can be applied to the prediction of time series. The main advantage of the LSM is that it projects the input data in a high-dimensional dynamical space and therefore simple learning methods can be used to train the readout. In this paper we present an experimental investigation of an approach for the computation of time series prediction by employing Liquid State Machines (LSM) in the modeling of a predictor in a case study for short-term and long-term electricity demand forecasting. Results of this investigation are promising, considering the error to stop training the readout, the number of iterations of training of the readout and that no strategy of seasonal adjustment or preprocessing of data was achieved to extract non-correlated data out of the time series.
System for Prediction of Non Stationary Time Series based on the Wavelet Radi...IJECEIAES
This paper proposes and examines the performance of a hybrid model called the wavelet radial bases function neural networks (WRBFNN). The model will be compared its performance with the wavelet feed forward neural networks (WFFN model by developing a prediction or forecasting system that considers two types of input formats: input9 and input17, and also considers 4 types of non-stationary time series data. The MODWT transform is used to generate wavelet and smooth coefficients, in which several elements of both coefficients are chosen in a particular way to serve as inputs to the NN model in both RBFNN and FFNN models. The performance of both WRBFNN and WFFNN models is evaluated by using MAPE and MSE value indicators, while the computation process of the two models is compared using two indicators, many epoch, and length of training. In stationary benchmark data, all models have a performance with very high accuracy. The WRBFNN9 model is the most superior model in nonstationary data containing linear trend elements, while the WFFNN17 model performs best on non-stationary data with the non-linear trend and seasonal elements. In terms of speed in computing, the WRBFNN model is superior with a much smaller number of epochs and much shorter training time.
Multi-task learning using non-linear autoregressive models and recurrent neur...IJECEIAES
Tide level forecasting plays an important role in environmental management and development. Current tide level forecasting methods are usually implemented for solving single task problems, that is, a model built based on the tide level data at an individual location is only used to forecast tide level of the same location but is not used for tide forecasting at another location. This study proposes a new method for tide level prediction at multiple locations simultaneously. The method combines nonlinear autoregressive moving average with exogenous inputs (NARMAX) model and recurrent neural networks (RNNs), and incorporates them into a multi-task learning (MTL) framework. Experiments are designed and performed to compare single task learning (STL) and MTL with and without using non-linear autoregressive models. Three different RNN variants, namely, long short- term memory (LSTM), gated recurrent unit (GRU) and bidirectional LSTM (BiLSTM) are employed together with non-linear autoregressive models. A case study on tide level forecasting at many different geographical locations (5 to 11 locations) is conducted. Experimental results demonstrate that the proposed architectures outperform the classical single-task prediction methods.
A Time Series ANN Approach for Weather Forecastingijctcm
Weather forecasting is most challenging problem around the world. There are various reason because of its experimented values in meteorology, but it is also a typical unbiased time series forecasting problem in scientific research. A lots of methods proposed by various scientists. The motive behind research is to predict more accurate. This paper contribute the same using artificial neural network (ANN) and simulated in MATLAB to predict two important weather parameters i.e. maximum and minimum temperature. The model has been trained using past 60 years of real data collected from(1901-1960) and tested over 40 years to forecast maximum and minimum temperature. The results based on mean square error function (MSE) confirm, this model which is based on multilayer perceptron has the potential to successful application to weather forecasting
Time Series Forecasting Using Novel Feature Extraction Algorithm and Multilay...Editor IJCATR
Time series forecasting is important because it can often provide the foundation for decision making in a large variety of fields. A tree-ensemble method, referred to as time series forest (TSF), is proposed for time series classification. The approach is based on the concept of data series envelopes and essential attributes generated by a multilayer neural network... These claims are further investigated by applying statistical tests. With the results presented in this article and results from related investigations that are considered as well, we want to support practitioners or scholars in answering the following question: Which measure should be looked at first if accuracy is the most important criterion, if an application is time-critical, or if a compromise is needed? In this paper demonstrated feature extraction by novel method can improvement in time series data forecasting process
Forecasting Electric Energy Demand using a predictor model based on Liquid St...Waqas Tariq
Electricity demand forecasts are required by companies who need to predict their customers’ demand, and by those wishing to trade electricity as a commodity on financial markets. It is hard to find the right prediction method for a given application if not a prediction expert. Recent works show that Liquid State Machines (LSM’s) can be applied to the prediction of time series. The main advantage of the LSM is that it projects the input data in a high-dimensional dynamical space and therefore simple learning methods can be used to train the readout. In this paper we present an experimental investigation of an approach for the computation of time series prediction by employing Liquid State Machines (LSM) in the modeling of a predictor in a case study for short-term and long-term electricity demand forecasting. Results of this investigation are promising, considering the error to stop training the readout, the number of iterations of training of the readout and that no strategy of seasonal adjustment or preprocessing of data was achieved to extract non-correlated data out of the time series.
Forecasting Electric Energy Demand using a predictor model based on Liquid St...Waqas Tariq
Electricity demand forecasts are required by companies who need to predict their customers’ demand, and by those wishing to trade electricity as a commodity on financial markets. It is hard to find the right prediction method for a given application if not a prediction expert. Recent works show that Liquid State Machines (LSM’s) can be applied to the prediction of time series. The main advantage of the LSM is that it projects the input data in a high-dimensional dynamical space and therefore simple learning methods can be used to train the readout. In this paper we present an experimental investigation of an approach for the computation of time series prediction by employing Liquid State Machines (LSM) in the modeling of a predictor in a case study for short-term and long-term electricity demand forecasting. Results of this investigation are promising, considering the error to stop training the readout, the number of iterations of training of the readout and that no strategy of seasonal adjustment or preprocessing of data was achieved to extract non-correlated data out of the time series.
A novel wind power prediction model using graph attention networks and bi-dir...IJECEIAES
Today, integrating wind energy forecasting is an important area of research due to the erratic nature of wind. To achieve this goal, we propose a new model of wind speed prediction based on graph attention networks (GAT), we added a new attention mechanism and a learnable adjacency matrix to the GAT structure to obtain attention scores for each weather variable. The results of the GAT-based model are merged with the bi-directional deep learning long and short-term memory (BiLSTM) layer to take advantage of the geographic and temporal properties of historical weather data. The experiments and analyzes are carried out using precise meteorological data collected from wind farms in the Moroccan city of Tetouan. We show that the proposed model can learn complex input-output correlations of meteorological data more efficiently than previous wind speed prediction algorithms. Due to the resulting attention weights, the model also provides more information about the main weather factors for the evaluated forecast work.
In the present paper the experimental study of
Nanotechnology involves high cost for Lab set-up and the
experimentation processes were also slow. Attempt has also
been made to discuss the contributions towards the societal
change in the present convergence of Nano-systems and
information technologies. one cannot rely on experimental
nanotechnology alone. As such, the Computer- simulations and
modeling are one of the foundations of computational
nanotechnology. The computer modeling and simulations
were also referred as computational experimentations. The
accuracy of such Computational nano-technology based
experiment generally depends on the accuracy of the following
things: Intermolecular interaction, Numerical models and
Simulation schemes used. The essence of nanotechnology is
therefore size and control because of the diversity of
applications the plural term nanotechnology is preferred by
some nevertheless they all share the common feature of control
at the nanometer scale the latter focusing on the observation
and study of phenomena at the nanometer scale. In this paper,
a brief study of Computer-Simulation techniques as well as
some Experimental result
SSA-based hybrid forecasting models and applicationsjournalBEEI
This study attempted to combine SSA (Singular Spectrum Analysis) with other methods to improve the performance of forecasting model for time series with a complex pattern. This work discussed two modifications of TLSAR (Two-Level Seasonal Autoregressive) modeling by considering the SSA decomposition results, namely TLSNN (Two-Level Seasonal Neural Network) and TLCSNN (Two-Level Complex Seasonal Neural Network). TLSAR consisted of a linear trend, harmonic, and autoregressive component. In contrast, the two proposed hybrid approaches consisted of flexible trend function, harmonic, and neural networks. Trend and harmonic function were considered as the deterministic part identified based on SSA decomposition. Meanwhile, NN was intended to handle the nonlinearity relationship in the stochastic part. These two SSA-based hybrid models were contemplated to be more flexible than TLSAR and more applicable to the series with an intricate pattern. The experimental studies to the monthly accidental deaths in USA and daily electricity load Jawa-Bali showed that the proposed SSA-based hybrid model reduced RMSE for the testing data from that obtained by TLSAR model up to 95%.
Short term residential load forecasting using long short-term memory recurre...IJECEIAES
Load forecasting plays an essential role in power system planning. The efficiency and reliability of the whole power system can be increased with proper planning and organization. Residential load forecasting is indispensable due to its increasing role in the smart grid environment. Nowadays, smart meters can be deployed at the residential level for collecting historical data consumption of residents. Although the employment of smart meters ensures large data availability, the inconsistency of load data makes it challenging and taxing to forecast accurately. Therefore, the traditional forecasting techniques may not suffice the purpose. However, a deep learning forecasting network-based long short-term memory (LSTM) is proposed in this paper. The powerful nonlinear mapping capabilities of RNN in time series make it effective along with the higher learning capabilities of long sequences of LSTM. The proposed method is tested and validated through available real-world data sets. A comparison of LSTM is then made with two traditionally available techniques, exponential smoothing and auto-regressive integrated moving average model (ARIMA). Real data from 12 houses over three months is used to evaluate and validate the performance of load forecasts performed using the three mentioned techniques. LSTM model has achieved the best results due to its higher capability of memorizing large data in time series-based predictions.
Human activity recognition with self-attentionIJECEIAES
In this paper, a self-attention based neural network architecture to address human activity recognition is proposed. The dataset used was collected using smartphone. The contribution of this paper is using a multi-layer multi-head self-attention neural network architecture for human activity recognition and compared to two strong baseline architectures, which are convolutional neural network (CNN) and long-short term network (LSTM). The dropout rate, positional encoding and scaling factor are also been investigated to find the best model. The results show that proposed model achieves a test accuracy of 91.75%, which is a comparable result when compared to both the baseline models.
Optimal artificial neural network configurations for hourly solar irradiation...IJECEIAES
Solar energy is widely used in order to generate clean electric energy. However, due to its intermittent nature, this resource is only inserted in a limited way within the electrical networks. To increase the share of solar energy in the energy balance and allow better management of its production, it is necessary to know precisely the available solar potential at a fine time step to take into account all these stochastic variations. In this paper, a comparison between different artificial neural network (ANN) configurations is elaborated to estimate the hourly solar irradiation. An investigation of the optimal neurons and layers is investigated. To this end, feedforward neural network, cascade forward neural network and fitting neural network have been applied for this purpose. In this context, we have used different meteorological parameters to estimate the hourly global solar irirradiation in the region of Laghouat, Algeria. The validation process shows that choosing the cascade forward neural network two inputs gives an R2 value equal to 97.24% and an normalized root mean square error (NRMSE) equals to 0.1678 compared to the results of three inputs, which gives an R2 value equaled to 95.54% and an NRMSE equals to 0.2252. The comparison between different existing methods in literature show the goodness of the proposed models.
A Novel Neuroglial Architecture for Modelling Singular Perturbation System IJECEIAES
This work develops a new modular architecture that emulates a recentlydiscovered biological paradigm. It originates from the human brain where the information flows along two different pathways and is processed along two time scales: one is a fast neural network (NN) and the other is a slow network called the glial network (GN). It was found that the neural network is powered and controlled by the glial network. Based on our biological knowledge of glial cells and the powerful concept of modularity, a novel approach called artificial neuroglial Network (ANGN) was designed and an algorithm based on different concepts of modularity was also developed. The implementation is based on the notion of multi-time scale systems. Validation is performed through an asynchronous machine (ASM) modeled in the standard singularly perturbed form. We apply the geometrical approach, based on Gerschgorin’s circle theorem (GCT), to separate the fast and slow variables, as well as the singular perturbation method (SPM) to determine the reduced models. This new architecture makes it possible to obtain smaller networks with less complexity and better performance.
A systematic review on sequence-to-sequence learning with neural network and ...IJECEIAES
We develop a precise writing survey on sequence-to-sequence learning with neural network and its models. The primary aim of this report is to enhance the knowledge of the sequence-to-sequence neural network and to locate the best way to deal with executing it. Three models are mostly used in sequence-to-sequence neural network applications, namely: recurrent neural networks (RNN), connectionist temporal classification (CTC), and attention model. The evidence we adopted in conducting this survey included utilizing the examination inquiries or research questions to determine keywords, which were used to search for bits of peer-reviewed papers, articles, or books at scholastic directories. Through introductory hunts, 790 papers, and scholarly works were found, and with the assistance of choice criteria and PRISMA methodology, the number of papers reviewed decreased to 16. Every one of the 16 articles was categorized by their contribution to each examination question, and they were broken down. At last, the examination papers experienced a quality appraisal where the subsequent range was from 83.3% to 100%. The proposed systematic review enabled us to collect, evaluate, analyze, and explore different approaches of implementing sequence-to-sequence neural network models and pointed out the most common use in machine learning. We followed a methodology that shows the potential of applying these models to real-world applications.
RAINFALL PREDICTION USING DATA MINING TECHNIQUES - A SURVEYcsandit
Rainfall is considered as one of the major components of the hydrological process; it takes
significant part in evaluating drought and flooding events. Therefore, it is important to have an
accurate model for rainfall prediction. Recently, several data-driven modeling approaches have
been investigated to perform such forecasting tasks as multilayer perceptron neural networks
(MLP-NN). In fact, the rainfall time series modeling (SARIMA) involvesimportant temporal
dimensions. In order to evaluate the incomes of both models, statistical parameters were used to
make the comparison between the two models. These parameters include the Root Mean Square
Error RMSE, Mean Absolute Error MAE, Coefficient Of Correlation CC and BIAS. Two-Third
of the data was used for training the model and One-third for testing.
Earthquake trend prediction using long short-term memory RNNIJECEIAES
The prediction of a natural calamity such as earthquakes has been an area of interest for a long time but accurate results in earthquake forecasting have evaded scientists, even leading some to deem it intrinsically impossible to forecast them accurately. In this paper an attempt to forecast earthquakes and trends using a data of a series of past earthquakes. A type of recurrent neural network called Long Short-Term Memory (LSTM) is used to model the sequence of earthquakes. The trained model is then used to predict the future trend of earthquakes. An ordinary Feed Forward Neural Network (FFNN) solution for the same problem was done for comparison. The LSTM neural network was found to outperform the FFNN. The R^2 score of the LSTM is better than the FFNN’s by 59%.
RAINFALL PREDICTION USING DATA MINING TECHNIQUES - A SURVEYcscpconf
Rainfall is considered as one of the major components of the hydrological process; it takes significant part in evaluating drought and flooding events. Therefore, it is important to have anaccurate model for rainfall prediction. Recently, several data-driven modeling approaches havebeen investigated to perform such forecasting tasks as multilayer perceptron neural networks
(MLP-NN). In fact, the rainfall time series modeling (SARIMA) involvesimportant temporal dimensions. In order to evaluate the incomes of both models, statistical parameters were used to
make the comparison between the two models. These parameters include the Root Mean Square Error RMSE, Mean Absolute Error MAE, Coefficient Of Correlation CC and BIAS. Two-Third of the data was used for training the model and One-third for testing.
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
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A novel wind power prediction model using graph attention networks and bi-dir...IJECEIAES
Today, integrating wind energy forecasting is an important area of research due to the erratic nature of wind. To achieve this goal, we propose a new model of wind speed prediction based on graph attention networks (GAT), we added a new attention mechanism and a learnable adjacency matrix to the GAT structure to obtain attention scores for each weather variable. The results of the GAT-based model are merged with the bi-directional deep learning long and short-term memory (BiLSTM) layer to take advantage of the geographic and temporal properties of historical weather data. The experiments and analyzes are carried out using precise meteorological data collected from wind farms in the Moroccan city of Tetouan. We show that the proposed model can learn complex input-output correlations of meteorological data more efficiently than previous wind speed prediction algorithms. Due to the resulting attention weights, the model also provides more information about the main weather factors for the evaluated forecast work.
In the present paper the experimental study of
Nanotechnology involves high cost for Lab set-up and the
experimentation processes were also slow. Attempt has also
been made to discuss the contributions towards the societal
change in the present convergence of Nano-systems and
information technologies. one cannot rely on experimental
nanotechnology alone. As such, the Computer- simulations and
modeling are one of the foundations of computational
nanotechnology. The computer modeling and simulations
were also referred as computational experimentations. The
accuracy of such Computational nano-technology based
experiment generally depends on the accuracy of the following
things: Intermolecular interaction, Numerical models and
Simulation schemes used. The essence of nanotechnology is
therefore size and control because of the diversity of
applications the plural term nanotechnology is preferred by
some nevertheless they all share the common feature of control
at the nanometer scale the latter focusing on the observation
and study of phenomena at the nanometer scale. In this paper,
a brief study of Computer-Simulation techniques as well as
some Experimental result
SSA-based hybrid forecasting models and applicationsjournalBEEI
This study attempted to combine SSA (Singular Spectrum Analysis) with other methods to improve the performance of forecasting model for time series with a complex pattern. This work discussed two modifications of TLSAR (Two-Level Seasonal Autoregressive) modeling by considering the SSA decomposition results, namely TLSNN (Two-Level Seasonal Neural Network) and TLCSNN (Two-Level Complex Seasonal Neural Network). TLSAR consisted of a linear trend, harmonic, and autoregressive component. In contrast, the two proposed hybrid approaches consisted of flexible trend function, harmonic, and neural networks. Trend and harmonic function were considered as the deterministic part identified based on SSA decomposition. Meanwhile, NN was intended to handle the nonlinearity relationship in the stochastic part. These two SSA-based hybrid models were contemplated to be more flexible than TLSAR and more applicable to the series with an intricate pattern. The experimental studies to the monthly accidental deaths in USA and daily electricity load Jawa-Bali showed that the proposed SSA-based hybrid model reduced RMSE for the testing data from that obtained by TLSAR model up to 95%.
Short term residential load forecasting using long short-term memory recurre...IJECEIAES
Load forecasting plays an essential role in power system planning. The efficiency and reliability of the whole power system can be increased with proper planning and organization. Residential load forecasting is indispensable due to its increasing role in the smart grid environment. Nowadays, smart meters can be deployed at the residential level for collecting historical data consumption of residents. Although the employment of smart meters ensures large data availability, the inconsistency of load data makes it challenging and taxing to forecast accurately. Therefore, the traditional forecasting techniques may not suffice the purpose. However, a deep learning forecasting network-based long short-term memory (LSTM) is proposed in this paper. The powerful nonlinear mapping capabilities of RNN in time series make it effective along with the higher learning capabilities of long sequences of LSTM. The proposed method is tested and validated through available real-world data sets. A comparison of LSTM is then made with two traditionally available techniques, exponential smoothing and auto-regressive integrated moving average model (ARIMA). Real data from 12 houses over three months is used to evaluate and validate the performance of load forecasts performed using the three mentioned techniques. LSTM model has achieved the best results due to its higher capability of memorizing large data in time series-based predictions.
Human activity recognition with self-attentionIJECEIAES
In this paper, a self-attention based neural network architecture to address human activity recognition is proposed. The dataset used was collected using smartphone. The contribution of this paper is using a multi-layer multi-head self-attention neural network architecture for human activity recognition and compared to two strong baseline architectures, which are convolutional neural network (CNN) and long-short term network (LSTM). The dropout rate, positional encoding and scaling factor are also been investigated to find the best model. The results show that proposed model achieves a test accuracy of 91.75%, which is a comparable result when compared to both the baseline models.
Optimal artificial neural network configurations for hourly solar irradiation...IJECEIAES
Solar energy is widely used in order to generate clean electric energy. However, due to its intermittent nature, this resource is only inserted in a limited way within the electrical networks. To increase the share of solar energy in the energy balance and allow better management of its production, it is necessary to know precisely the available solar potential at a fine time step to take into account all these stochastic variations. In this paper, a comparison between different artificial neural network (ANN) configurations is elaborated to estimate the hourly solar irradiation. An investigation of the optimal neurons and layers is investigated. To this end, feedforward neural network, cascade forward neural network and fitting neural network have been applied for this purpose. In this context, we have used different meteorological parameters to estimate the hourly global solar irirradiation in the region of Laghouat, Algeria. The validation process shows that choosing the cascade forward neural network two inputs gives an R2 value equal to 97.24% and an normalized root mean square error (NRMSE) equals to 0.1678 compared to the results of three inputs, which gives an R2 value equaled to 95.54% and an NRMSE equals to 0.2252. The comparison between different existing methods in literature show the goodness of the proposed models.
A Novel Neuroglial Architecture for Modelling Singular Perturbation System IJECEIAES
This work develops a new modular architecture that emulates a recentlydiscovered biological paradigm. It originates from the human brain where the information flows along two different pathways and is processed along two time scales: one is a fast neural network (NN) and the other is a slow network called the glial network (GN). It was found that the neural network is powered and controlled by the glial network. Based on our biological knowledge of glial cells and the powerful concept of modularity, a novel approach called artificial neuroglial Network (ANGN) was designed and an algorithm based on different concepts of modularity was also developed. The implementation is based on the notion of multi-time scale systems. Validation is performed through an asynchronous machine (ASM) modeled in the standard singularly perturbed form. We apply the geometrical approach, based on Gerschgorin’s circle theorem (GCT), to separate the fast and slow variables, as well as the singular perturbation method (SPM) to determine the reduced models. This new architecture makes it possible to obtain smaller networks with less complexity and better performance.
A systematic review on sequence-to-sequence learning with neural network and ...IJECEIAES
We develop a precise writing survey on sequence-to-sequence learning with neural network and its models. The primary aim of this report is to enhance the knowledge of the sequence-to-sequence neural network and to locate the best way to deal with executing it. Three models are mostly used in sequence-to-sequence neural network applications, namely: recurrent neural networks (RNN), connectionist temporal classification (CTC), and attention model. The evidence we adopted in conducting this survey included utilizing the examination inquiries or research questions to determine keywords, which were used to search for bits of peer-reviewed papers, articles, or books at scholastic directories. Through introductory hunts, 790 papers, and scholarly works were found, and with the assistance of choice criteria and PRISMA methodology, the number of papers reviewed decreased to 16. Every one of the 16 articles was categorized by their contribution to each examination question, and they were broken down. At last, the examination papers experienced a quality appraisal where the subsequent range was from 83.3% to 100%. The proposed systematic review enabled us to collect, evaluate, analyze, and explore different approaches of implementing sequence-to-sequence neural network models and pointed out the most common use in machine learning. We followed a methodology that shows the potential of applying these models to real-world applications.
RAINFALL PREDICTION USING DATA MINING TECHNIQUES - A SURVEYcsandit
Rainfall is considered as one of the major components of the hydrological process; it takes
significant part in evaluating drought and flooding events. Therefore, it is important to have an
accurate model for rainfall prediction. Recently, several data-driven modeling approaches have
been investigated to perform such forecasting tasks as multilayer perceptron neural networks
(MLP-NN). In fact, the rainfall time series modeling (SARIMA) involvesimportant temporal
dimensions. In order to evaluate the incomes of both models, statistical parameters were used to
make the comparison between the two models. These parameters include the Root Mean Square
Error RMSE, Mean Absolute Error MAE, Coefficient Of Correlation CC and BIAS. Two-Third
of the data was used for training the model and One-third for testing.
Earthquake trend prediction using long short-term memory RNNIJECEIAES
The prediction of a natural calamity such as earthquakes has been an area of interest for a long time but accurate results in earthquake forecasting have evaded scientists, even leading some to deem it intrinsically impossible to forecast them accurately. In this paper an attempt to forecast earthquakes and trends using a data of a series of past earthquakes. A type of recurrent neural network called Long Short-Term Memory (LSTM) is used to model the sequence of earthquakes. The trained model is then used to predict the future trend of earthquakes. An ordinary Feed Forward Neural Network (FFNN) solution for the same problem was done for comparison. The LSTM neural network was found to outperform the FFNN. The R^2 score of the LSTM is better than the FFNN’s by 59%.
RAINFALL PREDICTION USING DATA MINING TECHNIQUES - A SURVEYcscpconf
Rainfall is considered as one of the major components of the hydrological process; it takes significant part in evaluating drought and flooding events. Therefore, it is important to have anaccurate model for rainfall prediction. Recently, several data-driven modeling approaches havebeen investigated to perform such forecasting tasks as multilayer perceptron neural networks
(MLP-NN). In fact, the rainfall time series modeling (SARIMA) involvesimportant temporal dimensions. In order to evaluate the incomes of both models, statistical parameters were used to
make the comparison between the two models. These parameters include the Root Mean Square Error RMSE, Mean Absolute Error MAE, Coefficient Of Correlation CC and BIAS. Two-Third of the data was used for training the model and One-third for testing.
Similar to Efficiency of recurrent neural networks for seasonal trended time series modelling (20)
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
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voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
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Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
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grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
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deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
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negative consequences on the system's protection, stability, and security.
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including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
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Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
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and cost-free nature. The power output of these systems relies on solar cell
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An efficient security framework for intrusion detection and prevention in int...IJECEIAES
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Developing a smart system for infant incubators using the internet of things ...IJECEIAES
This research is developing an incubator system that integrates the internet of things and artificial intelligence to improve care for premature babies. The system workflow starts with sensors that collect data from the incubator. Then, the data is sent in real-time to the internet of things (IoT) broker eclipse mosquito using the message queue telemetry transport (MQTT) protocol version 5.0. After that, the data is stored in a database for analysis using the long short-term memory network (LSTM) method and displayed in a web application using an application programming interface (API) service. Furthermore, the experimental results produce as many as 2,880 rows of data stored in the database. The correlation coefficient between the target attribute and other attributes ranges from 0.23 to 0.48. Next, several experiments were conducted to evaluate the model-predicted value on the test data. The best results are obtained using a two-layer LSTM configuration model, each with 60 neurons and a lookback setting 6. This model produces an R 2 value of 0.934, with a root mean square error (RMSE) value of 0.015 and a mean absolute error (MAE) of 0.008. In addition, the R 2 value was also evaluated for each attribute used as input, with a result of values between 0.590 and 0.845.
A review on internet of things-based stingless bee's honey production with im...IJECEIAES
Honey is produced exclusively by honeybees and stingless bees which both are well adapted to tropical and subtropical regions such as Malaysia. Stingless bees are known for producing small amounts of honey and are known for having a unique flavor profile. Problem identified that many stingless bees collapsed due to weather, temperature and environment. It is critical to understand the relationship between the production of stingless bee honey and environmental conditions to improve honey production. Thus, this paper presents a review on stingless bee's honey production and prediction modeling. About 54 previous research has been analyzed and compared in identifying the research gaps. A framework on modeling the prediction of stingless bee honey is derived. The result presents the comparison and analysis on the internet of things (IoT) monitoring systems, honey production estimation, convolution neural networks (CNNs), and automatic identification methods on bee species. It is identified based on image detection method the top best three efficiency presents CNN is at 98.67%, densely connected convolutional networks with YOLO v3 is 97.7%, and DenseNet201 convolutional networks 99.81%. This study is significant to assist the researcher in developing a model for predicting stingless honey produced by bee's output, which is important for a stable economy and food security.
A trust based secure access control using authentication mechanism for intero...IJECEIAES
The internet of things (IoT) is a revolutionary innovation in many aspects of our society including interactions, financial activity, and global security such as the military and battlefield internet. Due to the limited energy and processing capacity of network devices, security, energy consumption, compatibility, and device heterogeneity are the long-term IoT problems. As a result, energy and security are critical for data transmission across edge and IoT networks. Existing IoT interoperability techniques need more computation time, have unreliable authentication mechanisms that break easily, lose data easily, and have low confidentiality. In this paper, a key agreement protocol-based authentication mechanism for IoT devices is offered as a solution to this issue. This system makes use of information exchange, which must be secured to prevent access by unauthorized users. Using a compact contiki/cooja simulator, the performance and design of the suggested framework are validated. The simulation findings are evaluated based on detection of malicious nodes after 60 minutes of simulation. The suggested trust method, which is based on privacy access control, reduced packet loss ratio to 0.32%, consumed 0.39% power, and had the greatest average residual energy of 0.99 mJoules at 10 nodes.
Fuzzy linear programming with the intuitionistic polygonal fuzzy numbersIJECEIAES
In real world applications, data are subject to ambiguity due to several factors; fuzzy sets and fuzzy numbers propose a great tool to model such ambiguity. In case of hesitation, the complement of a membership value in fuzzy numbers can be different from the non-membership value, in which case we can model using intuitionistic fuzzy numbers as they provide flexibility by defining both a membership and a non-membership functions. In this article, we consider the intuitionistic fuzzy linear programming problem with intuitionistic polygonal fuzzy numbers, which is a generalization of the previous polygonal fuzzy numbers found in the literature. We present a modification of the simplex method that can be used to solve any general intuitionistic fuzzy linear programming problem after approximating the problem by an intuitionistic polygonal fuzzy number with n edges. This method is given in a simple tableau formulation, and then applied on numerical examples for clarity.
The performance of artificial intelligence in prostate magnetic resonance im...IJECEIAES
Prostate cancer is the predominant form of cancer observed in men worldwide. The application of magnetic resonance imaging (MRI) as a guidance tool for conducting biopsies has been established as a reliable and well-established approach in the diagnosis of prostate cancer. The diagnostic performance of MRI-guided prostate cancer diagnosis exhibits significant heterogeneity due to the intricate and multi-step nature of the diagnostic pathway. The development of artificial intelligence (AI) models, specifically through the utilization of machine learning techniques such as deep learning, is assuming an increasingly significant role in the field of radiology. In the realm of prostate MRI, a considerable body of literature has been dedicated to the development of various AI algorithms. These algorithms have been specifically designed for tasks such as prostate segmentation, lesion identification, and classification. The overarching objective of these endeavors is to enhance diagnostic performance and foster greater agreement among different observers within MRI scans for the prostate. This review article aims to provide a concise overview of the application of AI in the field of radiology, with a specific focus on its utilization in prostate MRI.
Seizure stage detection of epileptic seizure using convolutional neural networksIJECEIAES
According to the World Health Organization (WHO), seventy million individuals worldwide suffer from epilepsy, a neurological disorder. While electroencephalography (EEG) is crucial for diagnosing epilepsy and monitoring the brain activity of epilepsy patients, it requires a specialist to examine all EEG recordings to find epileptic behavior. This procedure needs an experienced doctor, and a precise epilepsy diagnosis is crucial for appropriate treatment. To identify epileptic seizures, this study employed a convolutional neural network (CNN) based on raw scalp EEG signals to discriminate between preictal, ictal, postictal, and interictal segments. The possibility of these characteristics is explored by examining how well timedomain signals work in the detection of epileptic signals using intracranial Freiburg Hospital (FH), scalp Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) databases, and Temple University Hospital (TUH) EEG. To test the viability of this approach, two types of experiments were carried out. Firstly, binary class classification (preictal, ictal, postictal each versus interictal) and four-class classification (interictal versus preictal versus ictal versus postictal). The average accuracy for stage detection using CHB-MIT database was 84.4%, while the Freiburg database's time-domain signals had an accuracy of 79.7% and the highest accuracy of 94.02% for classification in the TUH EEG database when comparing interictal stage to preictal stage.
Analysis of driving style using self-organizing maps to analyze driver behaviorIJECEIAES
Modern life is strongly associated with the use of cars, but the increase in acceleration speeds and their maneuverability leads to a dangerous driving style for some drivers. In these conditions, the development of a method that allows you to track the behavior of the driver is relevant. The article provides an overview of existing methods and models for assessing the functioning of motor vehicles and driver behavior. Based on this, a combined algorithm for recognizing driving style is proposed. To do this, a set of input data was formed, including 20 descriptive features: About the environment, the driver's behavior and the characteristics of the functioning of the car, collected using OBD II. The generated data set is sent to the Kohonen network, where clustering is performed according to driving style and degree of danger. Getting the driving characteristics into a particular cluster allows you to switch to the private indicators of an individual driver and considering individual driving characteristics. The application of the method allows you to identify potentially dangerous driving styles that can prevent accidents.
Hyperspectral object classification using hybrid spectral-spatial fusion and ...IJECEIAES
Because of its spectral-spatial and temporal resolution of greater areas, hyperspectral imaging (HSI) has found widespread application in the field of object classification. The HSI is typically used to accurately determine an object's physical characteristics as well as to locate related objects with appropriate spectral fingerprints. As a result, the HSI has been extensively applied to object identification in several fields, including surveillance, agricultural monitoring, environmental research, and precision agriculture. However, because of their enormous size, objects require a lot of time to classify; for this reason, both spectral and spatial feature fusion have been completed. The existing classification strategy leads to increased misclassification, and the feature fusion method is unable to preserve semantic object inherent features; This study addresses the research difficulties by introducing a hybrid spectral-spatial fusion (HSSF) technique to minimize feature size while maintaining object intrinsic qualities; Lastly, a soft-margins kernel is proposed for multi-layer deep support vector machine (MLDSVM) to reduce misclassification. The standard Indian pines dataset is used for the experiment, and the outcome demonstrates that the HSSF-MLDSVM model performs substantially better in terms of accuracy and Kappa coefficient.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
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Hierarchical Digital Twin of a Naval Power SystemKerry Sado
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CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
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Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Efficiency of recurrent neural networks for seasonal trended time series modelling
1. International Journal of Electrical and Computer Engineering (IJECE)
Vol. 13, No. 6, December 2023, pp. 6586~6594
ISSN: 2088-8708, DOI: 10.11591/ijece.v13i6.pp6586-6594 6586
Journal homepage: http://ijece.iaescore.com
Efficiency of recurrent neural networks for seasonal trended
time series modelling
Rida El Abassi1
, Jaafar Idrais1
, Abderrahim Sabour1,2
1
IMI Laboratory, Department of Mathematics, Faculty of Sciences, Ibn Zohr University, Agadir, Morocco
2
Department of Computer Science, High School of Technology, Ibn Zohr University, Agadir, Morocco
Article Info ABSTRACT
Article history:
Received May 4, 2022
Revised Feb 9, 2023
Accepted Mar 9, 2023
Seasonal time series with trends are the most common data sets used in
forecasting. This work focuses on the automatic processing of a
non-pre-processed time series by studying the efficiency of recurrent neural
networks (RNN), in particular both long short-term memory (LSTM), and
bidirectional long short-term memory (Bi-LSTM) extensions, for modelling
seasonal time series with trend. For this purpose, we are interested in the
learning stability of the established systems using the mean average
percentage error (MAPE) as a measure. Both simulated and real data were
examined, and we have found a positive correlation between the signal
period and the system input vector length for stable and relatively efficient
learning. We also examined the white noise impact on the learning
performance.
Keywords:
Automatic learning
Long short-term memory
Machine learning
Recurrent neural network
Time series This is an open access article under the CC BY-SA license.
Corresponding Author:
Rida El Abassi
Laboratory of Mathematical and Computer Engineering, Faculty of Sciences, Ibn Zohr University
Agadir, Morocco
Email: rida.elabassi@edu.uiz.ac.ma
1. INTRODUCTION
The analysis of time series represents a source of knowledge and information given the amount of
data generated through technical and technological development, which multiplies the fields of application
for this discipline. In the field of time series, researchers tend to propose models describing the underlying
relationship of the generator process and to forecast time series [1]. The seasonal and trend components are
characteristics of several time series resulting from economic phenomena. Seasonality is considered as a
periodic and recurring pattern, while the trend component characterizes the long-term evolution of the time
series studied. The importance of accurate forecasting of seasonal time series trends is crucial for areas such
as marketing, inventory control and many other business sectors.
The traditional methods of time series analysis proceed with two main steps: decomposition, then
reconstitution of series to carry out the forecast [2]. This approach assumes that the structure of time series
can be decomposed into modellable elements [3]. There are three main components: the trend Tt, which
describes the long-term evolution and the phenomenon’s pattern, the seasonal component St, which
characterizes repetition over time, and the residual component Rt, which represent the noise [4].
In the 1970s, Box and Jenkins introduced another perspective on time series modelling [5], named
Box and Jenkins methodology, it is based on the Wold’s representation theorem [6]–[9]; in fact, once a
process is (weakly) stationary, it can be written as the weighted sum of past shocks. This is how the notion of
stationary becomes fundamental to the analysis process [10]. However, a seasonal and trend time series is
considered to be non-stationary and often needs to be made stationary, using a certain seasonal adjustment
method [11], before most modelling and forecasting processes take place.
2. Int J Elec & Comp Eng ISSN: 2088-8708
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Moreover, neural networks (NN) offer new perspectives [12]–[14] for modelling time series than
traditional seasonal autoregressive integrated moving average (SARIMA) models [15], [16]. The learning
mechanism allows to establish a neural architecture based on parameters such as the size of the input vector,
and the number of hidden layers. Indeed, NN have been widely applied to many fields through their
flexibility to design a network structure [17]. The fully connected NN (FNN) is a basic structure of neural
networks, Qi and Zhang [18] implemented this structure to seasonal time series with trends, indeed, they
conducted experiments by comparing the two models, autoregressive integrated moving average (ARIMA)
and FFN, which report that an FNN cannot directly model seasonality, however, a preprocessing step is
needed involving seasonal and trend adjustments for proper modelling. Liu et al. [19] also compares FFN and
ARIMA using the same type of simulated time series, this study concludes that by choosing rectified linear
unit (ReLU) or the linear activation function and Adam optimizer, the FFN model performs well.
The motivation for this works was inspired by Qi and Liu studies [18], [19], in which the authors
compare the performance of SARIMA to an FNN and a convolutional NN. In this paper, we plan to use a
recurrent neural network (RNN), in particular, long short-term memory (LSTM) and bidirectional long short-
term memory (Bi-LSTM) extensions. However, their experiments and conclusions are inadequate for our
purposes.
The aim of our study is to find a modelling method such that users do not have to worry about
preprocessing time series. Thus, the initial motivation of this paper is to develop a machine learning tool to
predict time series data without manual intervention, using recurrent neural networks. The main problem
is to find a general-purpose modelling method or algorithm that can handle seasonality, trends and
auto-correlations in time series data. It is important to note that the initial question was about the choice of
these parameters, in particular the size of the input vector and the number of hidden layers for additive and
multiplicative signals.
2. METHOD
2.1. Principle of LSTM and Bi-LSTM structures
Extensions of recurrent neural networks (RNNs) such as LSTMs are the most feasible solutions
since, they are directed to the problem of the gradient disappearance by managing short-and long-term
memory. They anticipate future predictions based on various highlighted characteristics present in the
dataset. LSTMs can remember or forget things precisely. Data collected on progressive timescales is
presented as time series, and let to make predictions, while LSTMs are proposed as a stable methodology. In
this type of design, the model passes the past protection state to the next stage of the layout. Since RNNs can
only store a limited amount of information for long-term memory storage, LSTMs cells are used with RNNs
[20]. They overcome the difficulties of leakage gradient and explosion gradient and have the ability to
support long-term dependencies by replacing the hidden layers of RNN with memory cells. The LSTM block
contains three gates [21] and each gate corresponds to a processing step. Standard recurrent neural
architectures, like LSTM, treat the inputs in one direction only and ignore the possessed information about
the future. The bi-directional LSTM (Bi-LSTM) model responds to this issue in its operating process [22].
For the Bi-LSTM topology [23]–[26], the information flows in two directions as illustrated in
Figure 1, taking into account the temporal dynamics of the vectors of past and future inputs. Standard RNN’s
hidden neurons are split forward and backward. The basic structure of Bi-LSTM [27] is unfolded in three-
time steps: forward pass, backward pass, and weight update.
Figure 1. Example of Bi-LSTM structure
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2.2. Processing strategy
The first questions at the origin of this study were mainly related to the capacity of RNNs to model
the regularities of a signal, specifically the seasonality and the trend. Then by developing a neural model, we
realized that several parameters are put into the equation, namely, the size of the input vector, and the number
of hidden layers. This work empirically highlights a correlation between the period of the time series and the
size of the input vector for stable and relatively successful learning. This study is conducted on twenty-six
time series derived from a real phenomenon, the first characterizes the evolution of the number of passengers
in an international airport, which is a classic signal in the literature [4], it was the first signal for which the
two researchers George Box and Gwilym Jenkins established their methodology, then the second
characterizes the evolution of CO2 concentrations in the air measured during 1974 through 1987 [28]. In
another sense, it is important to note that the time series belong to two basic classes of models, namely the
additive and the multiplicative models, in order to analyze the robustness of the RNNs not only to the change
of fluctuation at the seasonal level but also to the impact of white the noise and then to draw conclusions on
the stability of the established systems.
It is important to note that the learning process will be carried out by a part of the signal noted
(Train), which represents nearly 80% of the size of the basic signal. However, 20% will represent the part
(Test), which allow us to measure the performance of the learning carried out via the mean absolute
percentage error (MAPE) given in (1). The next step is to make a prediction of 100 future observations in
order to analyze the prediction of the system and its ability to detect the regularities of the signal and under
what conditions, and if the system has taken into account the regularities of the signal (Tt and St). We apply
the low-frequency filter (the moving average) by changing the window l to determine the period of the
predicted signal. In Figure 2, we have displayed the whole layout of the proposed model.
𝑀𝐴𝑃𝐸 = (
1
𝑘
∑ |
𝑎𝑥−𝑓𝑥
𝑎𝑥
|
𝑘
𝑥=1 ) × 100 (1)
Figure 2. Layout of proposed method
2.3. Simulated data
The design of the methodology of this empirical analysis focuses on the use of several time series
with different periods and variance 𝜎2
of white noise, we generate the time series via (2) and (3).
𝑦(𝑡) = 𝑆𝐼(𝑡) + 𝑇(𝑡) + 𝐸(𝑡) (2)
𝑦(𝑡) = 𝑆𝐼(𝑡) × 𝑇(𝑡) + 𝐸(𝑡) (3)
In (2) and (3) are a characteristic of the additive (AM) and multiplicative (MM) model respectively, such that
the SI(t) is the seasonality index, Table 1 shows the measures adopted for MM and MA. T(t) is the linear
trend, and E(t) is the distribution error that follows the normal distribution 𝑁(0, 𝜎2
). Note that for each given
SI we assign 𝜎2
three values 𝜎=1, 5, 12. Indeed, controlling the seasonality index allows us to fix the period
of seasonality and then see the reaction of the established systems with respect to these changes. On the other
hand, the change 𝜎 allows us to test the robustness of the established assumptions with respect to the increase
of the white noise energy in the signal. Figure 3 shows an example of the time series that we have generated
with a seasonality index SI given in Table 1, for MM in Figure 3(a) and for an AM in Figure 3(b), the part T
that characterizes the trend is given by T(t)=0.8t+150 for any 𝑡 ∈ [0.359], i.e., this series as well as all the
others generated from both MM and AM will have 360 observations. The white noise variance 𝜎2
of this
time series is given by (𝜎2
= 144).
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Table 1. Seasonal indexes used for simulated monthly data
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
SI MM 0.8 0.85 0.87 0.95 0.99 0.97 0.96 1.04 0.99 1.07 1.25 1.85
SI AM 89 96 99 105 119 98 92 136 93 149 168 194
(a) (b)
Figure 3. Plot the simulated time series (a) through the multiplicative model and (b) through the additive
model
2.4. Real data
Figure 4 represents the real database; Figure 4(a) shows the monthly evolution of total passengers in
an international airport in the period from January 1949 to December 1960. Box and Jenkin applied their
methodology to this time series and proposed linear models of class SARIMA that they developed [5]. The
time series shows an upward tendency and a seasonality of period p=12 that changes in fluctuation in the
course of the time. While Figure 4(b) illustrates the evolution of the air’s CO2 concentration from May 1974
to September 1987.
(a) (b)
Figure 4. Plot the real time series (a) monthly evolution of total passengers in an international airport in the
period from January 1949 to December 1960 and (b) evolution of CO2 concentration in the air in the period
May 1974 to September 1987
2.5. Modeling strategy
The databases we manipulated in this study are univariate time series. We implemented two models
of recurrent neural networks in particular LSTM and Bi-LSTM, using libraries such as NumPy [29], Pandas
[30], Keras and TensorFlow [31]. For a given signal with fixed period p and white noise variance 𝜎2
, we
performed in the learning by train part while varying the size of the input vector 𝑣𝑒 and took the following
values: 3, 4, 9 and 12. In other words, we have performed for a given signal four tests, this allows us to note
the correlations of the different parameters of the system. Figure 5 shows the neural structure adapted for the
two models, LSTM and Bi-LSTM. Noting that the structure is the same for both models, it is composed of an
input layer with 𝑣𝑒 inputs and connected to 256 neurons of the first hidden layer. The neural structure has six
hidden layers, the choice of the number of neurons is based on the remark of Moolayil [32], concerning the
number of hidden layers, which is one of the questions of this study. How to choose the number of hidden
layers intelligently in relation to the particularity of the signal to guarantee the performance of the learning.
We conducted experiments in this direction, but they did not lead to consistent results.
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Figure 5. RNN (LSTM and Bi-LSTM) structure with 256 input neurons and 7 hidden layers
Table 2 shows the different parameters of the neural architectures established. For the RNNs model
LSTM and its extension Bi-LSTM, the activation function is the ReLU, the optimization algorithm held is the
Adam's and the cost function employed is the mean-squared error (MSE). The learning algorithm tries to
minimize the cost function, which characterizes the distance between real and predicted values at the same
time, by adjusting the weights and bias of the system. The starting point of these parameters impacts the
learning performance, a decision is made to initialize the weights and bias of the neural architecture to the
same values for all performed tests.
Table 2. Parameter settings of the studied approaches
Models Parameters Values
LSTM and Bi-LSTM Input
Output
Hidden Layer
Neurons
Optimizer
Features
activation function
Loss function
Training epochs
𝑣𝑒
1
7
256-128-64-32-16-8-4
Adam
1
ReLU
MSE
500
3. RESULTS AND DISCUSSION
3.1. Simulated data results and discussion
We established LSTM and Bi-LSTM to try to answer the question reported in section 2. We adopted
the same parameters indicated in Table 2 of section 2 for all the neural architectures established. The purpose
of this study does not take into consideration the comparison of the different architectures such as Adam
algorithm, adaptive gradient algorithm (AdaGrad) and stochastic gradient descent (SGD) or the different
existing cost functions. Table 3 shows the results of the tests carried out using the methodology reported in
section 2 to generate the time series, according to AM and MM characterized respectively by the formulas 2
and 3. We raise two remarks: firstly, there is a correlation between the period of the signal and the size of the
input vector 𝑣𝑒, meaning that, to guarantee the relative performance of the learning, it is more appropriate to
choose 𝑣𝑒 = 𝑝, and this is for the two extensions of the recurrent neural networks LSTM and Bi-LSTM.
Secondly, the white noise impacts the learning performance.
Figure 6 shows the learning result for AM, Figures 6(a) and 6(b) illustrate the performance of the
LSTM and Bi-LSTM models, respectively. The MAPE, as shown in Table 3, is of order 0.53 and 0.49,
respectively. Figure 7 characterizes the learning results for MM, Figures 7(a) and 7(b) illustrate the
performance of the LSTM and Bi-LSTM models, respectively. The MAPE, as shown in Table 3, is of the
order 0.17 and 0.09 respectively.
Remember that the comparison of the two models LSTM and Bi-LSTM is not the goal of this work.
The initial question was mainly focused on the stability of learning, and to ensure an adequate model for
signals, characterized by a seasonal and a trend via RNNs models. Two interesting results are deduced: first,
a significant correlation exists between the size of the input vector of the system 𝑣𝑒 and the period of the
signal, second, the noise has an impact on the learning.
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3.2. Real data results and discussion
We used the same parameters used previously as shown in Table 2 and the same RNNs on the
two-time series, both additive and multiplicative, as described in subsection 3.3. We applied the low
frequency filter (the moving average) to determine the period, we conduct tests by systematically changing
the size of the input vector. The results of the system learning for the real data shows the potential of neural
networks to model this class of time series by choosing appropriate parameters, in particular the size of the
input vector, Figure 8(a) illustrates the performance of the LSTM models, the MAPE is of order 0.04, while
Figure 8(b) shows the prediction of the two neural systems for 100 future observations. Moreover,
Figure 9(a) shows the performance of the Bi-LSTM model, the MAPE is of order 0.05 while Figure 9(b)
shows the prediction of the two neural systems for 100 future observations.
(a) (b)
Figure 8. LSTM model results for total passengers in an international airport data (a) predicted values versus
true values on the training data and (b) prediction of 100 future observations
(a) (b)
Figure 9. Bi-LSTM model results for evolution of CO2 concentration in the air data (a) predicted values
versus true values on the training data and (b) prediction of 100 future observations
It is important to note that the learning performance depends on the size of the input vector ve,
which corroborates the conclusion made for the simulated data, indeed, we did the learning by varying ve, for
ve=12 the system becomes efficient compared to other 𝑣𝑒 values. The first multiplicative signal of the
monthly evolution of passengers has period p=12 [5], for ve equal to 3, 6 and 9, MAPE is of the order, 12.35,
4.21 and 13.93 respectively, and this for LSTM model, the choice of ve=12 allows an optimal performance
of the order MAPE=0.04. The second additive signal has period p=12 [28], for 𝑣𝑒 equal to 3, 6, 9 and 12,
MAPE is of the order, 10.26, 6.74, 8.31, and 0.05 respectively, and this for Bi-LSTM model. The prediction
of 100 future observations shows clearly that the system was able to learn the different features for the
multiplicative and additive signals, such as the variation of seasonal component fluctuations over time.
Liu et al. study [19], adopts models such as, the convolutional neural network (CNN), FNN and a
non-pooling CNN. Lui’s study [19] also made a comparative study on the optimizer parameter using several
types such as, Adadelta, AdaGrad, Adam, and SGD as well as the activation function namely, ReLU, Tanh,
linear. They concluded that the choice of system parameters impacts learning performance, by indicating that
choosing ReLU or linear activation functions and the Adam optimizer increases performance. Concerning
this paper, we focused the research on other parameters of the system, specifically, the size of the input
vector using RNN models.
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To evaluate the performance of the neural network system, we made a comparison with the
autoregressive moving average (ARMA) model, ARMA requires additional preprocessing to make both time
series stationary. We created a 12-lag difference to remove the seasonality and then a 2-lag difference to
remove the trends. The final model for the total passengers in an international airport data is ARMA with
p=12 and q=1, which were selected by the autocorrelation function, the partial autocorrelation function and
the Bayesian information criterion (BIC). For this model, the MAPE measure is of order 1.39. We find that
the LSTM has obtained much better MAPE value than the ARMA.
4. CONCLUSION
Analyzing and modelling time series allows the extraction of knowledge. In the present study, we
have introduced the modelling of seasonal time series with a trend via a supervised learning technique, in
particular the RNN method. For this, we have established both LSTM and Bi-LSTM models in order to
propose an approach to construct neural systems allowing relatively efficient modelling. We conducted tests
on real and simulated time series, and we simulated the additive and multiplicative classes, in order to test the
ability of the established systems to detect the change in the fluctuation of the seasonal component over time.
Based on 80% of the data, the two RNN extensions were able to predict the rest of the series, which was then
validated with the remaining 20%. Tests are performed by varying the period p and 𝜎2
(the variance of the
noise component), and we noted a significant correlation between the input vector size ve and the period p.
Indeed, to ensure relatively efficient learning, we recommend choosing the input vector size ve equal to the
signal period p. We have also concluded that noise has an impact on learning performance, as the increase of
MAPE error depends on the noise component.
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BIOGRAPHIES OF AUTHORS
Rida El Abassi he received the master’s degree in Applied Mathematical Sciences
in Engineering Sciences at the University Ibn Zohr, Ph.D. student at the University Ibn Zoher
Agadir Morocco, Mathematical and Computer Engineering Laboratory (IMI). His research
interests include the applications of the extraction of knowledge on big data and artificial
intelligence. He can be contacted at email: rida.elabassi@edu.uiz.ac.ma.
Jaafar Idrais is a computer science engineer with a bachelor’s degree in distributed
computing systems. He obtained his Ph.D. in computer science from the University Ibn Zohr
Agadir in 2022. His research focuses on online social network analysis and knowledge extraction
from virtual communities. He is a researcher at the University Ibn Zohr Agadir. Mathematical
Engineering and Computer Science Laboratory (IMI Lab) under the Faculty of Science of
Agadir. He can be contacted at email: jaafar.idrais@edu.uiz.ac.ma.
Abderrahim Sabour received his Ph.D. degree in Computer Science from the
University Mohammed VAgdal in 2007. His research interests include artificial intelligence and
digital sociology. He is presently working as a Researcher Professor in the Department of
Computer Science, Higher School of Technology, Ibn Zohr University, Agadir, Morocco. He can
be contacted at email: ab.sabour@uiz.ac.ma.