The document presents a framework for telecommunication traffic demand forecasting. It aims to extend the forecasting abilities of Detecon NetWork products and provide a theoretical and practical basis for forecasting methods. Several forecasting methods are classified and some are implemented, including Triple Exponential Smoothing (TES), periodic Double Exponential Smoothing (DES), and periodic Linear Regression (LinReg). Tests are performed on real operator data to evaluate the methods under different conditions like varying input data, missing data positions, and aggregation levels. The results show that TES is the most universal method, periodic LinReg is the fastest, and combining methods can improve accuracy. The work provides guidance on data preprocessing and outlines opportunities to integrate the methods into products
Software Cost Estimation Using Clustering and Ranking SchemeEditor IJMTER
Software cost estimation is an important task in the software design and development process.
Planning and budgeting tasks are carried out with reference to the software cost values. A variety of
software properties are used in the cost estimation process. Hardware, products, technology and
methodology factors are used in the cost estimation process. The software cost estimation quality is
measured with reference to the accuracy levels.
Software cost estimation is carried out using three types of techniques. They are regression based
model, anology based model and machine learning model. Each model has a set of technique for the
software cost estimation process. 11 cost estimation techniques fewer than 3 different categories are
used in the system. The Attribute Relational File Format (ARFF) is used maintain the software product
property values. The ARFF file is used as the main input for the system.
The proposed system is designed to perform the clustering and ranking of software cost
estimation methods. Non overlapped clustering technique is enhanced with optimal centroid estimation
mechanism. The system improves the clustering and ranking process accuracy. The system produces
efficient ranking results on software cost estimation methods.
It is a brief introduction to data assimilation (4D/3D VAR) and Old FSO (Forecast Sensitivity to Observation) system used in the National Center For Medium-Range Weather Forecasting, India. It was presented in Feb 2016.
Bayesian clinical trials: software and logisticsJohn Cook
This document discusses Bayesian clinical trial software developed by John D. Cook and colleagues at M. D. Anderson Cancer Center. It describes software tools for designing trials, such as for sample size calculation and dose finding, and for conducting trials, such as for safety monitoring and adaptive randomization. Challenges in clinical trial conduct software design are also outlined, such as handling changes during a trial. Resources for downloading or learning more about the software are provided.
Determination of Optimum Parameters Affecting the Properties of O RingsIRJET Journal
This document summarizes a study that used design of experiments (DOE) to determine the optimal parameters for curing and heat treating EPDM G2 flange O-rings. A 2^4 factorial design was used to test the effects of four factors (curing temperature, curing time, heat treatment temperature, and heat treatment time) on the mechanical properties of O-rings, including tensile strength, elongation at break, and load at break. Interaction plots and ANOVA were used to analyze the results. The analysis found a significant interaction between curing temperature and curing time for elongation at break and percentage elongation, but other interactions were insignificant. The study aims to optimize the curing and heat treatment parameters to achieve
On the Measurement of Test Collection ReliabilityJulián Urbano
The reliability of a test collection is proportional to the number of queries it contains. But building a collection with many queries is expensive, so researchers have to find a balance between reliability and cost. Previous work on the measurement of test collection reliability relied on data-based approaches that contemplated random what if scenarios, and provided indicators such as swap rates and Kendall tau correlations. Generalizability Theory was proposed as an alternative founded on analysis of variance that provides reliability indicators based on statistical theory. However, these reliability indicators are hard to interpret in practice, because they do not correspond to well known indicators like Kendall tau correlation. We empirically established these relationships based on data from over 40 TREC collections, thus filling the gap in the practical interpretation of Generalizability Theory. We also review the computation of these indicators, and show that they are extremely dependent on the sample of systems and queries used, so much that the required number of queries to achieve a certain level of reliability can vary in orders of magnitude. We discuss the computation of confidence intervals for these statistics, providing a much more reliable tool to measure test collection reliability. Reflecting upon all these results, we review a wealth of TREC test collections, arguing that they are possibly not as reliable as generally accepted and that the common choice of 50 queries is insufficient even for stable rankings.
Web engineering - Measuring Effort Prediction Power and AccuracyNosheen Qamar
This document discusses techniques for measuring the predictive accuracy of effort estimation models. It describes calculating the Mean Magnitude of Relative Error (MMRE) and Median Magnitude of Relative Error (MdMRE) to measure predictive power. To calculate predictive accuracy, a data set is divided into training and validation sets. The model predicts efforts for the validation set projects. MMRE and MdMRE are then calculated and aggregated to measure the model's predictive accuracy based on the validation set. Values below 0.25 indicate good predictive models. However, the best prediction technique depends on factors like the data set, so no single best technique has been agreed upon.
Software Cost Estimation Using Clustering and Ranking SchemeEditor IJMTER
Software cost estimation is an important task in the software design and development process.
Planning and budgeting tasks are carried out with reference to the software cost values. A variety of
software properties are used in the cost estimation process. Hardware, products, technology and
methodology factors are used in the cost estimation process. The software cost estimation quality is
measured with reference to the accuracy levels.
Software cost estimation is carried out using three types of techniques. They are regression based
model, anology based model and machine learning model. Each model has a set of technique for the
software cost estimation process. 11 cost estimation techniques fewer than 3 different categories are
used in the system. The Attribute Relational File Format (ARFF) is used maintain the software product
property values. The ARFF file is used as the main input for the system.
The proposed system is designed to perform the clustering and ranking of software cost
estimation methods. Non overlapped clustering technique is enhanced with optimal centroid estimation
mechanism. The system improves the clustering and ranking process accuracy. The system produces
efficient ranking results on software cost estimation methods.
It is a brief introduction to data assimilation (4D/3D VAR) and Old FSO (Forecast Sensitivity to Observation) system used in the National Center For Medium-Range Weather Forecasting, India. It was presented in Feb 2016.
Bayesian clinical trials: software and logisticsJohn Cook
This document discusses Bayesian clinical trial software developed by John D. Cook and colleagues at M. D. Anderson Cancer Center. It describes software tools for designing trials, such as for sample size calculation and dose finding, and for conducting trials, such as for safety monitoring and adaptive randomization. Challenges in clinical trial conduct software design are also outlined, such as handling changes during a trial. Resources for downloading or learning more about the software are provided.
Determination of Optimum Parameters Affecting the Properties of O RingsIRJET Journal
This document summarizes a study that used design of experiments (DOE) to determine the optimal parameters for curing and heat treating EPDM G2 flange O-rings. A 2^4 factorial design was used to test the effects of four factors (curing temperature, curing time, heat treatment temperature, and heat treatment time) on the mechanical properties of O-rings, including tensile strength, elongation at break, and load at break. Interaction plots and ANOVA were used to analyze the results. The analysis found a significant interaction between curing temperature and curing time for elongation at break and percentage elongation, but other interactions were insignificant. The study aims to optimize the curing and heat treatment parameters to achieve
On the Measurement of Test Collection ReliabilityJulián Urbano
The reliability of a test collection is proportional to the number of queries it contains. But building a collection with many queries is expensive, so researchers have to find a balance between reliability and cost. Previous work on the measurement of test collection reliability relied on data-based approaches that contemplated random what if scenarios, and provided indicators such as swap rates and Kendall tau correlations. Generalizability Theory was proposed as an alternative founded on analysis of variance that provides reliability indicators based on statistical theory. However, these reliability indicators are hard to interpret in practice, because they do not correspond to well known indicators like Kendall tau correlation. We empirically established these relationships based on data from over 40 TREC collections, thus filling the gap in the practical interpretation of Generalizability Theory. We also review the computation of these indicators, and show that they are extremely dependent on the sample of systems and queries used, so much that the required number of queries to achieve a certain level of reliability can vary in orders of magnitude. We discuss the computation of confidence intervals for these statistics, providing a much more reliable tool to measure test collection reliability. Reflecting upon all these results, we review a wealth of TREC test collections, arguing that they are possibly not as reliable as generally accepted and that the common choice of 50 queries is insufficient even for stable rankings.
Web engineering - Measuring Effort Prediction Power and AccuracyNosheen Qamar
This document discusses techniques for measuring the predictive accuracy of effort estimation models. It describes calculating the Mean Magnitude of Relative Error (MMRE) and Median Magnitude of Relative Error (MdMRE) to measure predictive power. To calculate predictive accuracy, a data set is divided into training and validation sets. The model predicts efforts for the validation set projects. MMRE and MdMRE are then calculated and aggregated to measure the model's predictive accuracy based on the validation set. Values below 0.25 indicate good predictive models. However, the best prediction technique depends on factors like the data set, so no single best technique has been agreed upon.
ESTIMATING HANDLING TIME OF SOFTWARE DEFECTScsandit
The problem of accurately predicting handling time for software defects is of great practical
importance. However, it is difficult to suggest a practical generic algorithm for such estimates,
due in part to the limited information available when opening a defect and the lack of a uniform
standard for defect structure. We suggest an algorithm to address these challenges that is
implementable over different defect management tools. Our algorithm uses machine learning
regression techniques to predict the handling time of defects based on past behaviour of similar
defects. The algorithm relies only on a minimal set of assumptions about the structure of the
input data. We show how an implementation of this algorithm predicts defect handling time with
promising accuracy results
Guidelines to Understanding Design of Experiment and Reliability Predictionijsrd.com
This paper will focus on how to plan experiments effectively and how to analyse data correctly. Practical and correct methods for analysing data from life testing will also be provided. This paper gives an extensive overview of reliability issues, definitions and prediction methods currently used in the industry. It defines different methods and correlations between these methods in order to make reliability comparison statements from different manufacturers' in easy way that may use different prediction methods and databases for failure rates. The paper finds however such comparison very difficult and risky unless the conditions for the reliability statements are scrutinized and analysed in detail.
This thesis describes a method to find a part of online data in an offline
document. This method is able to find the offline document that belongs
to the online data from a set of offline documents, or vice versa. In order to
optimize the mapping between the online and the offline data, an optimal rotation
and resizing of the online data is calculated. This is useful since it produces
a better mapping between online and offline data, which makes several methods
that are only applicable for online data available for offline data, and vice
versa.
Results show that this method can be used for finding the offline document that
belongs to certain online data, since it succeeded in 98.07% of the cases for
the used dataset. The results also show that computing the optimal rotation and
resize factor significantly improves the mapping between online and offline
data. This improvement is 6.56% for the used dataset.
An Effective Strategy of Firewall Based Matching AlgorithmIJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
This document discusses parameter tuning versus using default values for test data generation using the EvoSuite tool. It finds that while parameter tuning can improve performance on average, default values perform relatively well. The available search budget, or time and resources, has a strong impact on which parameter settings should be used. Parameter tuning becomes computationally expensive and does not always lead to significant improvements over default values.
IRJET- Software Bug Prediction using Machine Learning ApproachIRJET Journal
This document discusses using machine learning techniques to predict software bugs based on historical data. Specifically, it compares the performance of the Naive Bayes and J48 (Decision Tree) classifiers on bug prediction. The Naive Bayes and J48 classifiers are trained on datasets from real software projects containing product metrics and defect information. Their performance is evaluated based on accuracy, F-measure, recall, and precision. The results show that the J48 Decision Tree classifier has the best performance and is more accurate at predicting bugs compared to the Naive Bayes classifier. The authors conclude that machine learning is an effective approach for software bug prediction and can improve software quality if used early in the development process.
The End User Requirement for Project Management Software Accuracy IJECEIAES
This research explains the relationship between the end user requirement and accuracy of PMS (Project Management Software). The research aims are to analyze the PMS accuracy and measuring the probability of PMS accuracy in achieving ±1% of the end user requirement. The bias statistical method will be used to prove the PMS accuracy that based on the hypothesis testing. The result indicates the PMS is still accurate to be implemented in AcehIndonesia area projects that using the SNI (National Indonesia Standard as current method) with the accuracy index of ±7.5%. The achievement probability of reaching the end user requirement is still low of ±21.77%. In case of the PMS, the low achievement of the end user requirement is not only caused by the low accuracy of the PMS but also caused by the amount of variability error, which is influenced by the amount of variation of the project activity. In this study, we confirm that it is necessary to reconcile both conditions between the PMS accuracy and the end user requirements.
Machine Learning approaches are good in solving problems that have less information. In most cases, the
software domain problems characterize as a process of learning that depend on the various circumstances
and changes accordingly. A predictive model is constructed by using machine learning approaches and
classified them into defective and non-defective modules. Machine learning techniques help developers to
retrieve useful information after the classification and enable them to analyse data from different
perspectives. Machine learning techniques are proven to be useful in terms of software bug prediction. This
study used public available data sets of software modules and provides comparative performance analysis
of different machine learning techniques for software bug prediction. Results showed most of the machine
learning methods performed well on software bug datasets.
Applicability of Hooke’s and Jeeves Direct Search Solution Method to Metal c...ijiert bestjournal
Role of optimization in engineering design is prominent one with the advent of computers. Optimization has become a part of computer aided design activities. It is prima rily being used in those design activities in which the goal is not only to achieve just a feasible design,but also a des ign objective. In most engineering design activities,the design objective could be simply to minimize the cost of production or to maximize the efficiency of the production. An optimization algorithm is a procedure which is executed it eratively by comparing various solutions till the optimum or a satisfactory solution is found. In many industri al design activities,optimization is achieved indirectly by comparing a few chosen design solutions and accept ing the best solution. This simplistic approach never guarantees and optimization algorithms being with one or more d esign solutions supplied by the user and then iteratively check new design the true optimum solution. There ar e two distinct types of optimization algorithms which are in use today. First there are algorithms which are deterministic,with specific rules for moving from one solution to the other secondly,there are algorithms whi ch are stochastic transition rules.
Computational optimization, modelling and simulation: Recent advances and ove...Xin-She Yang
This document summarizes recent advances in computational optimization, modeling, and simulation. It discusses how optimization is important for engineering design and industrial applications to maximize profits and minimize costs. Metaheuristic algorithms and surrogate-based optimization techniques are becoming widely used for complex optimization problems. The workshop accepted papers that applied optimization, modeling, and simulation to diverse areas like production planning, mixed-integer programming, electromagnetics, and reliability analysis. Overall computational optimization and modeling have broad applications and continued research is needed in areas like metaheuristic convergence and surrogate modeling methods.
This document provides an overview of dimensional analysis, which is a technique used in engineering to relate physical quantities that influence a system. It describes how dimensional analysis identifies the relevant variables and forms dimensionless groups of variables. An example is provided to illustrate how dimensional analysis can be used to determine the unknown powers in an equation relating the force on a propeller blade to variables like its diameter, velocity, fluid density, and viscosity. Buckingham's pi theorems are explained as providing the theoretical basis for dimensional analysis.
This document provides a weekly outline of the content, activities, resources, and assessments for a Year 12 Mathematical Methods course over two school terms. The course covers topics including algebraic modelling, rates of change, derivatives, statistics, matrices, and linear programming. Students will learn concepts through lessons, modelling activities, investigations and practice questions, making use of textbooks, worksheets, online resources, and software. Their understanding will be evaluated through formative and summative tests, skills reviews, directed investigations, and mid-year and major exams.
One–day wave forecasts based on artificial neural networksJonathan D'Cruz
The document summarizes a study that uses artificial neural networks (ANNs) to generate 24-hour wave forecast based on wave buoy data from 6 locations. It trains ANNs using over 12 years of wave height data from the buoys as input, and forecasts wave heights up to 24 hours ahead as output. The ANNs are able to generate reliable 6-12 hour forecasts, but longer-term forecasts tend to underestimate peak heights or delay their timing. Real-time predictions starting in April 2005 showed similar trends.
Application of the analytic hierarchy process (AHP) for selection of forecast...Gurdal Ertek
In this paper, we described an application of the Analytic Hierarchy Process (AHP) for the ranking and selection of forecasting software. AHP is a multi-criteria decision making (MCDM) approach, which is based on the pair-wise comparison
of elements of a given set with respect to multiple criteria. Even though there are applications of the AHP to software selection problems, we have not encountered a study that involves forecasting software. We started our analysis by filtering
among forecasting software that were found on the Internet by undergraduate students as a part of a course project. Then we processed a second filtering step, where we reduced the number of software to be examined even further. Finally we
constructed the comparison matrices based upon the evaluations of three “semiexperts”, and obtained a ranking of forecasting software of the selected software using the Expert Choice software. We report our findings and our insights, together with the results of a sensitivity analysis.
http://research.sabanciuniv.edu.
This document discusses using statistical techniques to improve predictability in project performance. It provides three scenarios as examples: 1) Predicting critical activities on a schedule using a criticality index, 2) Estimating a project schedule and cost using Monte Carlo simulation, and 3) Building an early warning system for project monitoring and control. The document emphasizes that statistical methods can help project managers develop more accurate estimates and better manage project risks and performance.
IRJET - Neural Network based Leaf Disease Detection and Remedy Recommenda...IRJET Journal
This document describes a neural network-based system for detecting leaf diseases and recommending remedies. It uses a convolutional neural network (CNN) and deep learning techniques to classify images of plant leaves with different diseases. The system is trained on a dataset of 5000 leaf images across 4 disease classes. It aims to help farmers more easily identify leaf diseases and receive treatment recommendations without needing to directly contact experts. The document outlines the existing problems, proposed solution, literature review on related techniques like boosting and support vector machines, software and algorithms used including Python, Anaconda and Spyder. It also describes the implementation process involving modules for data loading, preprocessing, feature extraction using CNN, disease prediction, and recommending remedies.
This document summarizes Walter Shewhart's contributions to statistical process control and quality improvement methods in the 1920s. It discusses how Shewhart developed control charts to distinguish between assignable and chance causes of variation in processes. It also explains how Shewhart's methods influenced later techniques like statistical process control (SPC), six sigma, and Program Evaluation and Review Technique (PERT) analysis. The document concludes by recommending training employees in Shewhart's statistical analysis methods through courses like six sigma.
Estimating involves forming approximate notions of amounts, numbers, or positions without actual measurement. Accurate estimates are important for project planning, budgeting, and determining viability. Estimates become more accurate over the project lifecycle as more knowledge is gained. Common types of estimates include order-of-magnitude, budget, and definitive estimates. Top-down, bottom-up, and parametric methods are commonly used estimating approaches. Estimates should involve subject matter experts, use multiple methods, document assumptions, and apply contingency allowances. Regularly reviewing and updating estimates improves accuracy.
Mr. Muhammad Ahsan Nawaz worked as a software engineer at Canal Motors Faisalabad from November 2013 to February 2014. During his time there, he proved himself to be an intellectual, hard worker who was innovative and dedicated to his work. He was considered one of the best employees at the organization. The letter recommends him to pursue higher education abroad and wishes him good luck due to his abilities and potential to work as a team leader.
The document summarizes the Rottweiler field robot created by students at Rhine-Waal University of Applied Sciences. The Rottweiler is a small and efficient field robot that excels at navigation and plant observation. It has a dedicated microcomputer, IMU, stereo camera, and laser sensor. The robot can autonomously navigate, recognize and count plants using its powerful battery and sensors.
Germany has ambitious renewable energy targets of 80% renewable generation by 2050 and 35% by 2020 to reduce greenhouse gas emissions and transition to a sustainable energy system. This has led to a large increase in distributed renewable generation, especially solar PV, connected to the distribution grid. This is challenging grid operators as renewable generation introduces high variability that must be balanced. Pilot projects are exploring solutions like smart metering, demand response, and energy storage to improve grid observability, balance generation and load, and maximize grid capacity utilization in adapting distribution grids to the energy transition.
The Rottweiler is a small field robot created by Hochschule Rhein Waal for plant observation and navigation. It uses a hybrid navigation system to avoid obstacles using ultrasound, laser, and camera sensors. The robot can autonomously navigate, recognize, and count plants using its powerful battery and sensors. It has a Tegra K1 processing unit which provides smooth operation. The Rottweiler is lightweight, all-terrain, weatherproof, and has interchangeable drive systems, making it efficient and versatile.
ESTIMATING HANDLING TIME OF SOFTWARE DEFECTScsandit
The problem of accurately predicting handling time for software defects is of great practical
importance. However, it is difficult to suggest a practical generic algorithm for such estimates,
due in part to the limited information available when opening a defect and the lack of a uniform
standard for defect structure. We suggest an algorithm to address these challenges that is
implementable over different defect management tools. Our algorithm uses machine learning
regression techniques to predict the handling time of defects based on past behaviour of similar
defects. The algorithm relies only on a minimal set of assumptions about the structure of the
input data. We show how an implementation of this algorithm predicts defect handling time with
promising accuracy results
Guidelines to Understanding Design of Experiment and Reliability Predictionijsrd.com
This paper will focus on how to plan experiments effectively and how to analyse data correctly. Practical and correct methods for analysing data from life testing will also be provided. This paper gives an extensive overview of reliability issues, definitions and prediction methods currently used in the industry. It defines different methods and correlations between these methods in order to make reliability comparison statements from different manufacturers' in easy way that may use different prediction methods and databases for failure rates. The paper finds however such comparison very difficult and risky unless the conditions for the reliability statements are scrutinized and analysed in detail.
This thesis describes a method to find a part of online data in an offline
document. This method is able to find the offline document that belongs
to the online data from a set of offline documents, or vice versa. In order to
optimize the mapping between the online and the offline data, an optimal rotation
and resizing of the online data is calculated. This is useful since it produces
a better mapping between online and offline data, which makes several methods
that are only applicable for online data available for offline data, and vice
versa.
Results show that this method can be used for finding the offline document that
belongs to certain online data, since it succeeded in 98.07% of the cases for
the used dataset. The results also show that computing the optimal rotation and
resize factor significantly improves the mapping between online and offline
data. This improvement is 6.56% for the used dataset.
An Effective Strategy of Firewall Based Matching AlgorithmIJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
This document discusses parameter tuning versus using default values for test data generation using the EvoSuite tool. It finds that while parameter tuning can improve performance on average, default values perform relatively well. The available search budget, or time and resources, has a strong impact on which parameter settings should be used. Parameter tuning becomes computationally expensive and does not always lead to significant improvements over default values.
IRJET- Software Bug Prediction using Machine Learning ApproachIRJET Journal
This document discusses using machine learning techniques to predict software bugs based on historical data. Specifically, it compares the performance of the Naive Bayes and J48 (Decision Tree) classifiers on bug prediction. The Naive Bayes and J48 classifiers are trained on datasets from real software projects containing product metrics and defect information. Their performance is evaluated based on accuracy, F-measure, recall, and precision. The results show that the J48 Decision Tree classifier has the best performance and is more accurate at predicting bugs compared to the Naive Bayes classifier. The authors conclude that machine learning is an effective approach for software bug prediction and can improve software quality if used early in the development process.
The End User Requirement for Project Management Software Accuracy IJECEIAES
This research explains the relationship between the end user requirement and accuracy of PMS (Project Management Software). The research aims are to analyze the PMS accuracy and measuring the probability of PMS accuracy in achieving ±1% of the end user requirement. The bias statistical method will be used to prove the PMS accuracy that based on the hypothesis testing. The result indicates the PMS is still accurate to be implemented in AcehIndonesia area projects that using the SNI (National Indonesia Standard as current method) with the accuracy index of ±7.5%. The achievement probability of reaching the end user requirement is still low of ±21.77%. In case of the PMS, the low achievement of the end user requirement is not only caused by the low accuracy of the PMS but also caused by the amount of variability error, which is influenced by the amount of variation of the project activity. In this study, we confirm that it is necessary to reconcile both conditions between the PMS accuracy and the end user requirements.
Machine Learning approaches are good in solving problems that have less information. In most cases, the
software domain problems characterize as a process of learning that depend on the various circumstances
and changes accordingly. A predictive model is constructed by using machine learning approaches and
classified them into defective and non-defective modules. Machine learning techniques help developers to
retrieve useful information after the classification and enable them to analyse data from different
perspectives. Machine learning techniques are proven to be useful in terms of software bug prediction. This
study used public available data sets of software modules and provides comparative performance analysis
of different machine learning techniques for software bug prediction. Results showed most of the machine
learning methods performed well on software bug datasets.
Applicability of Hooke’s and Jeeves Direct Search Solution Method to Metal c...ijiert bestjournal
Role of optimization in engineering design is prominent one with the advent of computers. Optimization has become a part of computer aided design activities. It is prima rily being used in those design activities in which the goal is not only to achieve just a feasible design,but also a des ign objective. In most engineering design activities,the design objective could be simply to minimize the cost of production or to maximize the efficiency of the production. An optimization algorithm is a procedure which is executed it eratively by comparing various solutions till the optimum or a satisfactory solution is found. In many industri al design activities,optimization is achieved indirectly by comparing a few chosen design solutions and accept ing the best solution. This simplistic approach never guarantees and optimization algorithms being with one or more d esign solutions supplied by the user and then iteratively check new design the true optimum solution. There ar e two distinct types of optimization algorithms which are in use today. First there are algorithms which are deterministic,with specific rules for moving from one solution to the other secondly,there are algorithms whi ch are stochastic transition rules.
Computational optimization, modelling and simulation: Recent advances and ove...Xin-She Yang
This document summarizes recent advances in computational optimization, modeling, and simulation. It discusses how optimization is important for engineering design and industrial applications to maximize profits and minimize costs. Metaheuristic algorithms and surrogate-based optimization techniques are becoming widely used for complex optimization problems. The workshop accepted papers that applied optimization, modeling, and simulation to diverse areas like production planning, mixed-integer programming, electromagnetics, and reliability analysis. Overall computational optimization and modeling have broad applications and continued research is needed in areas like metaheuristic convergence and surrogate modeling methods.
This document provides an overview of dimensional analysis, which is a technique used in engineering to relate physical quantities that influence a system. It describes how dimensional analysis identifies the relevant variables and forms dimensionless groups of variables. An example is provided to illustrate how dimensional analysis can be used to determine the unknown powers in an equation relating the force on a propeller blade to variables like its diameter, velocity, fluid density, and viscosity. Buckingham's pi theorems are explained as providing the theoretical basis for dimensional analysis.
This document provides a weekly outline of the content, activities, resources, and assessments for a Year 12 Mathematical Methods course over two school terms. The course covers topics including algebraic modelling, rates of change, derivatives, statistics, matrices, and linear programming. Students will learn concepts through lessons, modelling activities, investigations and practice questions, making use of textbooks, worksheets, online resources, and software. Their understanding will be evaluated through formative and summative tests, skills reviews, directed investigations, and mid-year and major exams.
One–day wave forecasts based on artificial neural networksJonathan D'Cruz
The document summarizes a study that uses artificial neural networks (ANNs) to generate 24-hour wave forecast based on wave buoy data from 6 locations. It trains ANNs using over 12 years of wave height data from the buoys as input, and forecasts wave heights up to 24 hours ahead as output. The ANNs are able to generate reliable 6-12 hour forecasts, but longer-term forecasts tend to underestimate peak heights or delay their timing. Real-time predictions starting in April 2005 showed similar trends.
Application of the analytic hierarchy process (AHP) for selection of forecast...Gurdal Ertek
In this paper, we described an application of the Analytic Hierarchy Process (AHP) for the ranking and selection of forecasting software. AHP is a multi-criteria decision making (MCDM) approach, which is based on the pair-wise comparison
of elements of a given set with respect to multiple criteria. Even though there are applications of the AHP to software selection problems, we have not encountered a study that involves forecasting software. We started our analysis by filtering
among forecasting software that were found on the Internet by undergraduate students as a part of a course project. Then we processed a second filtering step, where we reduced the number of software to be examined even further. Finally we
constructed the comparison matrices based upon the evaluations of three “semiexperts”, and obtained a ranking of forecasting software of the selected software using the Expert Choice software. We report our findings and our insights, together with the results of a sensitivity analysis.
http://research.sabanciuniv.edu.
This document discusses using statistical techniques to improve predictability in project performance. It provides three scenarios as examples: 1) Predicting critical activities on a schedule using a criticality index, 2) Estimating a project schedule and cost using Monte Carlo simulation, and 3) Building an early warning system for project monitoring and control. The document emphasizes that statistical methods can help project managers develop more accurate estimates and better manage project risks and performance.
IRJET - Neural Network based Leaf Disease Detection and Remedy Recommenda...IRJET Journal
This document describes a neural network-based system for detecting leaf diseases and recommending remedies. It uses a convolutional neural network (CNN) and deep learning techniques to classify images of plant leaves with different diseases. The system is trained on a dataset of 5000 leaf images across 4 disease classes. It aims to help farmers more easily identify leaf diseases and receive treatment recommendations without needing to directly contact experts. The document outlines the existing problems, proposed solution, literature review on related techniques like boosting and support vector machines, software and algorithms used including Python, Anaconda and Spyder. It also describes the implementation process involving modules for data loading, preprocessing, feature extraction using CNN, disease prediction, and recommending remedies.
This document summarizes Walter Shewhart's contributions to statistical process control and quality improvement methods in the 1920s. It discusses how Shewhart developed control charts to distinguish between assignable and chance causes of variation in processes. It also explains how Shewhart's methods influenced later techniques like statistical process control (SPC), six sigma, and Program Evaluation and Review Technique (PERT) analysis. The document concludes by recommending training employees in Shewhart's statistical analysis methods through courses like six sigma.
Estimating involves forming approximate notions of amounts, numbers, or positions without actual measurement. Accurate estimates are important for project planning, budgeting, and determining viability. Estimates become more accurate over the project lifecycle as more knowledge is gained. Common types of estimates include order-of-magnitude, budget, and definitive estimates. Top-down, bottom-up, and parametric methods are commonly used estimating approaches. Estimates should involve subject matter experts, use multiple methods, document assumptions, and apply contingency allowances. Regularly reviewing and updating estimates improves accuracy.
Mr. Muhammad Ahsan Nawaz worked as a software engineer at Canal Motors Faisalabad from November 2013 to February 2014. During his time there, he proved himself to be an intellectual, hard worker who was innovative and dedicated to his work. He was considered one of the best employees at the organization. The letter recommends him to pursue higher education abroad and wishes him good luck due to his abilities and potential to work as a team leader.
The document summarizes the Rottweiler field robot created by students at Rhine-Waal University of Applied Sciences. The Rottweiler is a small and efficient field robot that excels at navigation and plant observation. It has a dedicated microcomputer, IMU, stereo camera, and laser sensor. The robot can autonomously navigate, recognize and count plants using its powerful battery and sensors.
Germany has ambitious renewable energy targets of 80% renewable generation by 2050 and 35% by 2020 to reduce greenhouse gas emissions and transition to a sustainable energy system. This has led to a large increase in distributed renewable generation, especially solar PV, connected to the distribution grid. This is challenging grid operators as renewable generation introduces high variability that must be balanced. Pilot projects are exploring solutions like smart metering, demand response, and energy storage to improve grid observability, balance generation and load, and maximize grid capacity utilization in adapting distribution grids to the energy transition.
The Rottweiler is a small field robot created by Hochschule Rhein Waal for plant observation and navigation. It uses a hybrid navigation system to avoid obstacles using ultrasound, laser, and camera sensors. The robot can autonomously navigate, recognize, and count plants using its powerful battery and sensors. It has a Tegra K1 processing unit which provides smooth operation. The Rottweiler is lightweight, all-terrain, weatherproof, and has interchangeable drive systems, making it efficient and versatile.
The document proposes a project to increase subscribers for the life:) service by developing and selling customized tablet PCs that can access the internet through the life:) online service. It suggests creating 7-inch and 10-inch tablet PCs running Android and with specifications like 8GB of storage and WiFi connectivity. Selling these tablets could provide a new type of product not currently on the Ukraine market and attract up to 1,650 new subscribers over time, helping more people access the internet through their tablet all day long using a well-equipped and stylish device.
Anand Nagarajan's resume summarizes his educational and professional background. He received a Master of Science in Information Technology from Fachhochschule Kiel in Germany and a Bachelor of Technology in Information Technology from SASTRA University in India. His work experience includes a master's thesis and internship at Bosch GmbH, where he analyzed SAP supply chain systems and developed databases. He also has qualifications in SAP ERP, programming languages, and IT frameworks.
Was ist Future Work?
Es ist das Konzept / Philosophie, das die Organisation auf die Herausforderungen des 21. Jahrhundert die McAfee in The 2nd maschine age beschreibt, vorbereitet und im Zentrum steht maximale Flexibilität und Wandelbarkeit.
Wesentliche Leitplanken sind hier aus unserer Sicht:
- Agile und flexible Strukturen mit sehr flachen Hierarchie entlang der Dimensionen Organisation, Places (d.h. keine Zonen, Bereiche oder Abteiliungen sondern flexible, maximal + ohne Rüstaufwand wandelbare Räume / Umgebung), Tools
- Activity Based Working – d.h. Arbeit ist kein Ort mehr, sondern man schafft ein für unterschiedliche Arbeitssituationen optimales Umfeld
- Ergebnis statt Präsenzkultur
- Förderung von sozialer Intelligenz und Kreativität (vgl. Oxford – „Was bleibt …“)
- Nutzung von Netzeffekten und Skalierung
- Nutzung des Netzeffektes und Skalierbarkeit
Die Präsentation beschreibt die Grundprinzipien entlang der Dimensionen "People", "Places" und "Tools" und zeigt einige Umsetzungsszenarien sowie ein geeignetes Vorgehensmodell auf. Für Rückfragen gerne direkt bei mir melden. Weitere Details auf: http://www.detecon.com/de/Hot_Topics/future-work
High Scalability by Example – How can Web-Architecture scale like Facebook, T...Robert Mederer
Skalierbarkeit bedeutet hohes Aufkommen von Traffic, Daten, Userbase, IO, Parallelverarbeitung und Concurrency, aber wie funktioniert dies bei den bekannten Web 2.0 Plattformen. Wie wird skaliert – horizontal oder vertikal, im Client-Layer, Service-Layer oder im Backend-Layer? Welche Rolle spielt Caching, NoSQL, Clustering und MapReduce bei der Skalierbarkeit? Wie wirkt sich die Skalierbarkeit in Sachen Konsistenz vs. Verfügbarkeit vs. Network Toleranz aus? Der Vortrag geht vergleichend auf verschiedene Konzepte von Skalierbarkeit ein und erläutert anhand von Beispielen wie mit pragmatischen Mitteln eine skalierbare Architektur erreicht werden kann.
This document discusses load forecasting techniques. It begins with an introduction that defines load forecasting as predicting future electricity demand on the power grid. This helps energy providers plan for needs and ensure capacity. Various forecasting methods are mentioned, including time series analysis, machine learning, and statistical modeling.
The document then covers exponential smoothing techniques for load forecasting, including simple exponential smoothing for data without trends or seasons, Holt's method for incorporating trends, and Holt-Winters for trends and seasons. It provides the procedures for simple exponential smoothing, including initializing the forecast, updating with a smoothing constant, and calculating error metrics.
Finally, the document shows the results of applying simple exponential smoothing to load data from New England over
IRJET - Intelligent Weather Forecasting using Machine Learning TechniquesIRJET Journal
This document discusses using machine learning techniques to forecast weather intelligently. It proposes using multi-target regression and recurrent neural network (RNN) models trained on historical weather data from Bangalore to predict future weather conditions like temperature, humidity, and precipitation. The data is first preprocessed before being fed to the models. The models are evaluated to accurately predict weather in the short term to help people like farmers and commuters without relying on expensive equipment.
Service Management: Forecasting Hydrogen Demandirrosennen
The document discusses various data science methodologies that can be used for forecasting hydrogen demand in the industrial sector. It covers time series forecasting methods like exponential smoothing, ARIMA, and Prophet. Machine learning regression techniques including linear, logistic, and support vector regression are presented. Deep learning neural networks such as RNNs and LSTMs are also discussed. The document advocates for hybrid and ensemble methods. Additional topics include forecasting with external factors, demand segmentation, real-time data integration, cross-validation, and continuous monitoring and adjustment. RNNs have shown effectiveness for hydrogen demand forecasting. Ensemble models can outperform single methods when applied to complex phenomena. Real-time data is critical for accurate forecasts.
IRJET- Overview of Forecasting TechniquesIRJET Journal
This document provides an overview of different forecasting techniques, including qualitative and quantitative methods. It discusses several qualitative techniques like the Delphi method, consumer market surveys, and jury of executive opinion. It also examines various quantitative techniques such as the moving average method, weighted moving average method, exponential smoothing, and least squares. The document serves to introduce students to common forecasting approaches and provide examples of each type of technique.
Improving the cosmic approximate sizing using the fuzzy logic epcu model al...IWSM Mensura
The document describes an experiment to improve the accuracy of the COSMIC functional size measurement (FSM) method for early stage software projects. It uses a fuzzy logic model called EPCU, which considers two variables - the perceived size of use cases and number of related objects of interest - to estimate functional size. The experiment applied the EPCU model and traditional "equal size bands" approach to estimate sizes for 14 use cases. The EPCU model initially underestimated sizes but accuracy improved when expanding the output range, demonstrating its sensitivity to variable definitions. Further experiments are needed with more test cases to validate the approach.
Forecasting cost and schedule performanceGlen Alleman
For credible decisions to be made, we need confidence intervals on all the numbers we use to make decisions.
These confidence intervals come from the underlying statistics and the related probabilities.
Statistical forecasting, using time series analysis of past performance, is mandatory for any credible discussion of project performance in the future.
IRJET- Error Reduction in Data Prediction using Least Square Regression MethodIRJET Journal
This document proposes a modification to the least squares regression method to reduce errors in data prediction. It divides the original data set into three parts, uses the first part to make predictions with least squares regression and fits those predictions to the second part of the data to minimize errors. It then validates the model on the third part of data and compares errors to the original least squares method. The proposed method shows reduced errors in prediction based on mean absolute error, mean relative error and root mean square error metrics in most test ranges of the validation data.
In the present paper, applicability and
capability of A.I techniques for effort estimation prediction has
been investigated. It is seen that neuro fuzzy models are very
robust, characterized by fast computation, capable of handling
the distorted data. Due to the presence of data non-linearity, it is
an efficient quantitative tool to predict effort estimation. The one
hidden layer network has been developed named as OHLANFIS
using MATLAB simulation environment.
Here the initial parameters of the OHLANFIS are
identified using the subtractive clustering method. Parameters of
the Gaussian membership function are optimally determined
using the hybrid learning algorithm. From the analysis it is seen
that the Effort Estimation prediction model developed using
OHLANFIS technique has been able to perform well over normal
ANFIS Model.
High dimensionality reduction on graphical dataeSAT Journals
Abstract In spite of the fact that graph embedding has been an intense instrument for displaying data natural structures, just utilizing all elements for data structures revelation may bring about noise amplification. This is especially serious for high dimensional data with little examples. To meet this test, a novel effective structure to perform highlight determination for graph embedding, in which a classification of graph implanting routines is given a role as a slightest squares relapse issue. In this structure, a twofold component selector is acquainted with normally handle the component cardinality at all squares detailing. The proposed strategy is quick and memory proficient. The proposed system is connected to a few graph embedding learning issues, counting administered, unsupervised and semi supervised graph embedding. Key Words:Efficient feature selection, High dimensional data, Sparse graph embedding, Sparse principal component analysis, Subproblem Optimization.
This document compares different data-driven modeling techniques for reservoir inflow analysis, specifically artificial neural networks (ANN) and M5 model trees. Traditional hydrological methods for inflow analysis were complex, time-consuming, and required extensive data collection. Data-driven techniques provide simpler alternatives by using attributes like direct rainfall-runoff data from surrounding rain gauge stations. The document outlines how ANNs and M5 model trees work, and finds that M5 model trees performed more accurately than ANNs for this reservoir inflow prediction task, as the model setting is easier, training is faster, and results are in a linear equation format.
Neural networks for the prediction and forecasting of water resources variablesJonathan D'Cruz
This document reviews the use of artificial neural networks (ANNs) for predicting and forecasting water resource variables. It outlines the key steps in developing ANN models, including choosing performance criteria, preprocessing and dividing data, determining appropriate model inputs and network architecture, optimizing connection weights through training, and validating models. Specifically, it focuses on feedforward networks with sigmoid transfer functions, which have been most widely used for predicting water resources variables.
EMPIRICAL APPLICATION OF SIMULATED ANNEALING USING OBJECT-ORIENTED METRICS TO...ijcsa
The work is about using Simulated Annealing Algorithm for the effort estimation model parameter
optimization which can lead to the reduction in the difference in actual and estimated effort used in model
development.
The model has been tested using OOP’s dataset, obtained from NASA for research purpose.The data set
based model equation parameters have been found that consists of two independent variables, viz. Lines of
Code (LOC) along with one more attribute as a dependent variable related to software development effort
(DE). The results have been compared with the earlier work done by the author on Artificial Neural
Network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS) and it has been observed that the
developed SA based model is more capable to provide better estimation of software development effort than
ANN and ANFIS
The document presents research on using neural networks to predict Earth Orientation Parameters (EOP) such as UT1-TAI. Three neural network models were tested:
1) Network 1 varied the number of neurons proportionally with increasing training sample size.
2) Network 2 kept the number of neurons constant while increasing sample size.
3) Network 3 used daily training data with 2 neurons and sample sizes of 4, 10, 20, and 365 days.
The goal was to minimize prediction error (RMSE) for horizons of 5-25 days by adjusting sample size and neurons. Results showed the best balance was needed between these factors, and that short-term prediction was possible within 10 days using
This document summarizes a study on short-term wind power forecasting for a wind farm in complex terrain in China. The study combines micro-scale computational fluid dynamics modeling with artificial neural networks to minimize forecast errors. Testing was performed from March 2012 to November 2012 with forecasts made every 15 minutes up to 46 hours ahead. Results showed the combined approach reduced mean absolute error by 5% and bias by 42% compared to using just the physical modeling alone.
In this deck from the Stanford HPC Conference, Peter Dueben from the European Centre for Medium-Range Weather Forecasts (ECMWF) presents: Machine Learning for Weather Forecasts.
"I will present recent studies that use deep learning to learn the equations of motion of the atmosphere, to emulate model components of weather forecast models and to enhance usability of weather forecasts. I will than talk about the main challenges for the application of deep learning in cutting-edge weather forecasts and suggest approaches to improve usability in the future."
Peter is contributing to the development and optimization of weather and climate models for modern supercomputers. He is focusing on a better understanding of model error and model uncertainty, on the use of reduced numerical precision that is optimised for a given level of model error, on global cloud- resolving simulations with ECMWF's forecast model, and the use of machine learning, and in particular deep learning, to improve the workflow and predictions. Peter has graduated in Physics and wrote his PhD thesis at the Max Planck Institute for Meteorology in Germany. He worked as Postdoc with Tim Palmer at the University of Oxford and has taken up a position as University Research Fellow of the Royal Society at the European Centre for Medium-Range Weather Forecasts (ECMWF) in 2017.
Watch the video: https://youtu.be/ks3fkRj8Iqc
Learn more: https://www.ecmwf.int/
and
http://www.hpcadvisorycouncil.com/events/2020/stanford-workshop/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Atmospheric Pollutant Concentration Prediction Based on KPCA BPijtsrd
PM2.5 prediction research has important significance for improving human health and atmospheric environmental quality, etc. This paper uses a model combining nuclear principal component analysis method and neural network to study the prediction problem of meteorological pollutant concentration, and compares the experimental results with the prediction results of the original neural network and the principal component analysis neural network. Based on the O3, CO, PM10, SO2, NO2 concentrations and parallel meteorological conditions data of Beijing from 2016 to 2020, the PM2.5 concentration was predicted. First, reduce the latitude of the data, and then use the KPCA BP neural network algorithm for training. The results show that the average absolute error, root mean square error and expected variance score of the combined model are relatively good, the generalization ability is strong, and the extreme value prediction is the best, which is better than that of the single model. Xin Lin | Bo Wang | Wenjing Ai "Atmospheric Pollutant Concentration Prediction Based on KPCA-BP" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-5 , August 2022, URL: https://www.ijtsrd.com/papers/ijtsrd51746.pdf Paper URL: https://www.ijtsrd.com/engineering/environment-engineering/51746/atmospheric-pollutant-concentration-prediction-based-on-kpcabp/xin-lin
Parkinson Disease Detection Using XGBoost and SVMIRJET Journal
This document presents research on using machine learning algorithms to detect Parkinson's disease using voice and speech parameters. The researchers used Support Vector Machine (SVM) and XGBoost algorithms to build models for Parkinson's disease detection and achieved 94.87% accuracy. Voice and speech data from patients was analyzed to identify important features for the models. SVM and XGBoost were able to accurately classify patients as having Parkinson's disease or not based on their voice and speech features, demonstrating the potential of artificial intelligence and machine learning techniques for Parkinson's disease diagnosis.
Automatically Estimating Software Effort and Cost using Computing Intelligenc...cscpconf
In the IT industry, precisely estimate the effort of each software project the development cost
and schedule are count for much to the software company. So precisely estimation of man
power seems to be getting more important. In the past time, the IT companies estimate the work
effort of man power by human experts, using statistics method. However, the outcomes are
always unsatisfying the management level. Recently it becomes an interesting topic if computing
intelligence techniques can do better in this field. This research uses some computing
intelligence techniques, such as Pearson product-moment correlation coefficient method and
one-way ANOVA method to select key factors, and K-Means clustering algorithm to do project
clustering, to estimate the software project effort. The experimental result show that using
computing intelligence techniques to estimate the software project effort can get more precise
and more effective estimation than using traditional human experts did
A Hierarchical Feature Set optimization for effective code change based Defec...IOSR Journals
This document summarizes research on using support vector machines (SVMs) for software defect prediction. It analyzes 11 datasets from NASA projects containing code metrics and defect information for modules. The researchers preprocessed the data by removing duplicate/inconsistent instances, constant attributes, and balancing the datasets. They used SVMs with 5-fold cross validation to classify modules as defective or non-defective, achieving an average accuracy of 70% across the datasets. The researchers conclude SVMs can effectively predict defects but note earlier studies using the NASA data may have overstated capabilities due to insufficient data preprocessing.
This document provides an overview of time series analysis and forecasting using neural networks. It discusses key concepts like time series components, smoothing methods, and applications. Examples are provided on using neural networks to forecast stock prices and economic time series. The agenda covers introduction to time series, importance, components, smoothing methods, applications, neural network issues, examples, and references.
Similar to Presentation_THESIS_Kryvoshapka_v1.4 (20)
1. We make ICT strategies work
Prof. Dr.-Ing. Thomas Bauschert , Dr. Mathias Schweigel, Oleksandr Kryvoshapka
Technische Universität Chemnitz, Detecon International GmbH
Feb 2016
A Framework for Telecommunication
Traffic Demand Forecasting
Short- and medium-term forecasts are required for activities that range from operations management to budgeting and selecting new research and development projects.
Long-term forecasts affect issues such as strategic planning.
Short- and medium-term forecasting is typically based on identifying, modeling, and extrapolating the patterns found in historical data.
The task of timing forecasts is to determine the time when an event will happen.
Frequency forecasts are aiming to determine quantities of events that will occur at certain period.
The continuance of an event is the reason for duration forecasts.
Monthly sales for the souvenir shop at a beach resort town in Queensland, Australia [a-little-book-of-r-for-time-series.readthedocs.org]
Periodicity is IMPORTANT!!! Trend
The horizontal direction of a smoothed time-series. Trends can be long-term pattern or dynamic in relatively short-term duration. Trend reflects the underlying growth or decline in the value of the variable. This variation pattern is present at least over several successive periods. Perception of the trend depends on the length of the observed series. If a time series does not show an increasing or decreasing pattern then such time-series called “stationary”.Seasonal variations
Patterns of change in a time-series within a period of no more than a year. These patterns tend to repeat themselves. It refers to short-term, relatively frequent variations, which are identified by the differences between the actual results and the trend line.
In real life, this pattern can repeat hourly, daily, weekly, monthly, yearly, etc. Seasonal variations is always has a known period sometimes called periodic variations. Generally related to factors such as weather, holidays and vacations and so on.
Cyclical variations
The variations of a time-series over periods longer than one year. They are not having a fixed period and often related to the current economic conditions. As a rule, the length of cycles is longer than the length of a season, and the magnitude of cycles usually much higher than the magnitude of seasonal patterns.
Usually cyclical variations are not present in the typical time-series.
Irregular variations
Unpredictable component of every time-series that makes it a random variable. Irregular variations in the data caused by unusual circumstances. In general, the duration if such variations is short.
There are two types of irregular variations can be specified: episodic and residual. Episodic fluctuations can be identified by nature of emergence. The residual fluctuations (chance fluctuations) cannot be identified. Of course, neither episodic nor residual variation can be projected into the future.
The advantages of Periodic DES and Periodic LinReg over normal DES and Linear Regression methods are:
The individual simplicity if original methods is kept.
The new periodic algorithms are able to forecast seasonal time series with (local or global) trend.
The disadvantages are next:
Overall complexity of the method is dependent from the periodicity L of the input data.
More input periods is required to produce adequate forecast.
Operators collect data usually on hourly or sub-hourly basis.To save storage space – aggregation to the higher level.
To forecast next hour can be interesting but not useful.Most used cases in short term forecasting.Forecast MAX to know if we need to increase the capacity of the cell.
Due to the lack of time, only some of the tests will be explained in the details
The main purpose of this test is to find out the impact of different amount of input data on the accuracy of implemented forecasting methods. The preliminary consideration is that more input data used – more accurate (in terms of MSEforecast) will be the forecasted values.
Accuracy measure problem: no distinguish between above or below certain value.