1) To understand the underlying structure of Time Series represented by sequence of observations by breaking it down to its components.
2) To fit a mathematical model and proceed to forecast the future.
Basic Concepts, Components of time series. The trend, Fitting of trend by least square method and moving average method, uses of time series in business.
Introduction - Objectives Of Studying Time Series Analysis - Variations In Time Series
- Methods Of Estimating Trend: Freehand Method - Moving Average Method - Semi-Average Method - Least
Square Method
The explosion of sensors in all types of devices from “smart” consumer wearables and appliances to complex machines on manufacturing floors has given rise to a requirement to quickly analyze vast quantities of sensor metrics to provide meaningful insights. From exploratory to predictive analytics, analyzing time-series data is essential to address inefficiencies, identify risks and improve operations.
In this presentation, we will see how you can conduct exploratory analytics of time-series data rapidly to gain insights into the performance of the machines being monitored. We will talk about how to look at data from multiple metrics together in a holistic way to hone in on anomalies and identify potential problems. Finally, we will cover algorithms and techniques to predict future trends for time-series metrics. Along the way, we will discuss useful tools and technologies to perform time-series data analysis in minutes.
ARIMA models provide another approach to time series forecasting. Exponential smoothing and ARIMA models are the two most widely-used approaches to time series forecasting, and provide complementary approaches to the problem. While exponential smoothing models were based on a description of trend and seasonality in the data, ARIMA models aim to describe the autocorrelations in the data.
Time Series basic concepts and ARIMA family of models. There is an associated video session along with code in github: https://github.com/bhaskatripathi/timeseries-autoregressive-models
https://drive.google.com/file/d/1yXffXQlL6i4ufQLSpFFrJgymhHNXL1Mf/view?usp=sharing
In this study, we have to project the airline travel for the next 12 months .The dataset used here is SASHELP.AIR which is Airline data and contains two variables – DATE and AIR( labeled as International Airline Travel).It contains the data from JAN 1949 to DEC 1960.
Basic Concepts, Components of time series. The trend, Fitting of trend by least square method and moving average method, uses of time series in business.
Introduction - Objectives Of Studying Time Series Analysis - Variations In Time Series
- Methods Of Estimating Trend: Freehand Method - Moving Average Method - Semi-Average Method - Least
Square Method
The explosion of sensors in all types of devices from “smart” consumer wearables and appliances to complex machines on manufacturing floors has given rise to a requirement to quickly analyze vast quantities of sensor metrics to provide meaningful insights. From exploratory to predictive analytics, analyzing time-series data is essential to address inefficiencies, identify risks and improve operations.
In this presentation, we will see how you can conduct exploratory analytics of time-series data rapidly to gain insights into the performance of the machines being monitored. We will talk about how to look at data from multiple metrics together in a holistic way to hone in on anomalies and identify potential problems. Finally, we will cover algorithms and techniques to predict future trends for time-series metrics. Along the way, we will discuss useful tools and technologies to perform time-series data analysis in minutes.
ARIMA models provide another approach to time series forecasting. Exponential smoothing and ARIMA models are the two most widely-used approaches to time series forecasting, and provide complementary approaches to the problem. While exponential smoothing models were based on a description of trend and seasonality in the data, ARIMA models aim to describe the autocorrelations in the data.
Time Series basic concepts and ARIMA family of models. There is an associated video session along with code in github: https://github.com/bhaskatripathi/timeseries-autoregressive-models
https://drive.google.com/file/d/1yXffXQlL6i4ufQLSpFFrJgymhHNXL1Mf/view?usp=sharing
In this study, we have to project the airline travel for the next 12 months .The dataset used here is SASHELP.AIR which is Airline data and contains two variables – DATE and AIR( labeled as International Airline Travel).It contains the data from JAN 1949 to DEC 1960.
Time Series Analysis - 2 | Time Series in R | ARIMA Model Forecasting | Data ...Simplilearn
This Time Series Analysis (Part-2) in R presentation will help you understand what is ARIMA model, what is correlation & auto-correlation and you will alose see a use case implementation in which we forecast sales of air-tickets using ARIMA and at the end, we will also how to validate a model using Ljung-Box text. A time series is a sequence of data being recorded at specific time intervals. The past values are analyzed to forecast a future which is time-dependent. Compared to other forecast algorithms, with time series we deal with a single variable which is dependent on time. So, lets deep dive into this presentation and understand what is time series and how to implement time series using R.
Below topics are explained in this " Time Series in R presentation " -
1. Introduction to ARIMA model
2. Auto-correlation & partial auto-correlation
3. Use case - Forecast the sales of air-tickets using ARIMA
4. Model validating using Ljung-Box test
Become an expert in data analytics using the R programming language in this data science certification training course. You’ll master data exploration, data visualization, predictive analytics and descriptive analytics techniques with the R language. With this data science course, you’ll get hands-on practice on R CloudLab by implementing various real-life, industry-based projects in the domains of healthcare, retail, insurance, finance, airlines, music industry, and unemployment.
Why learn Data Science with R?
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3. Wired.com points to a report by Glassdoor that the average salary of a data scientist is $118,709
4. Randstad reports that pay hikes in the analytics industry are 50% higher than IT
The Data Science with R is recommended for:
1. IT professionals looking for a career switch into data science and analytics
2. Software developers looking for a career switch into data science and analytics
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Learn more at: https://www.simplilearn.com/
Air traffic forecast serves as an important quantitative basis for airport planning - in particular for capacity planning CAPEX ,as well as for aeronautical and non-aeronautical revenue planning. High level decisions and planning in airports relies heavilly on future airport activity.
MFBLP Method Forecast for Regional Load Demand SystemCSCJournals
Load forecast plays an important role in planning and operation of a power system. The accuracy of the forecast value is necessary for economically efficient operation and also for effective control. This paper describes a method of modified forward backward linear predictor (MFBLP) for solving the regional load demand of New South Wales (NSW), Australia. The method is designed and simulated based on the actual load data of New South Wales, Australia. The accuracy of discussed method is obtained and comparison with previous methods is also reported.
This Project is helpful for Time Series Analysis Forecasting. Better accuracy and metrics
in short-term forecasting are provided for intermediate planning for the target to reduce
CO2 emissions. Implementing different models like Exponential techniques, Linear
statistical modeling, and Autoregressive are used to forecast the emissions and finally
deployed on Stream lit.
An ARIMAX model can be viewed as a multiple regression model with one or more autoregressive (AR) terms and/or one or more moving average (MA) terms. It is suitable for forecasting when data is stationary/non stationary, and multivariate with any type of data pattern, i.e., level/trend /seasonality/cyclicity. ARIMAX provides forecasted values of the target variables for user-specified time periods to illustrate results for planning, production, sales and other factors.
Air Passenger Prediction Using ARIMA Model AkarshAvinash
How has the Airline industry suffered during the pandemic? was a question that always stuck in my mind
when I saw articles on how travelling has been banned and movement of people not only from one country
to another country but also one state to another was being restricted. Hence as a statistics Student with a
curious mind I set out on a quest to find the effect of pandemic on the airline Industry. Tying statistics to
business problems that could benefit a business excites me. Hence I took up the initiative and called two
friends and decided to take their help in this task
we decided to get month wise domestic and international aviation data of the number of departures and
passengers in India during Jan 2010 to April 2022 from Airport Authorities of India website. We then took
this data cleaned, processed and transformed it to make it usable for our analysis. The analysis I suggested
to do for this objective was a familiar one which we had recently learnt in our fifth semester which was
Time series analysis under which we used the Auto Regressive Integrated Moving Average model which
creates a model that uses the past data to predict the future. As I am comfortable in coding I did the analysis
using R studio and python which has some excellent libraries to assist us in the analysis. We created the
model in such a way that the data could predict how the industry would behave if covid had not occurred.
We then compared the reality with the simulation which gave us some interesting interpretations. The results we found is that, international aviation industry on an average suffered five crores thirty three lakhs
per flight per month in losses and the domestic industry on an average suffered eighty two lakhs twenty
four thousand per flight per month in losses. But the key takeaway for the aviation industry from our
simulation vs reality analysis is that international travel is almost back on track after a major setback like
travel ban and it took 2 years and 3 months to do so whereas domestic travel is yet to recover.
I presented our findings and analysis to my statistics professor Mrs.Anwesha Roy also under whose
guidance we could come this far. She was thrilled with our work and encouraged us to get it published and
my team is currently working on it.
ARCH/GARCH model.ARCH/GARCH is a method to measure the volatility of the series, to model the noise term of ARIMA model. ARCH/GARCH incorporates new information and analyze the series based on the conditional variance where users can forecast future values with updated information. Here we used ARIMA-ARCH model to forecast moments. And forecast error 0.9%
Telecom Fraud Detection - Naive Bayes ClassificationMaruthi Nataraj K
To create a fraud management classification model that is powerful enough to handle the subscription fraud that the company has encountered and flexible enough to potentially apply to things that had not been witnessed yet
Hospital Market Segmentation using Cluster AnalysisMaruthi Nataraj K
To identify the hospitals’ segments with overall high sales. Then look for hospitals within that segment where the company sales of surgical equipments are low.
Maruti Suzuki India Ltd Financial Statement AnalysisMaruthi Nataraj K
Maruti Suzuki India Ltd Financial Statement Analysis
We have considered Tata Motors in whole as its competitor but it is advised to take the related segments for better results.
Show drafts
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
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Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
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Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
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By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
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🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
StarCompliance is a leading firm specializing in the recovery of stolen cryptocurrency. Our comprehensive services are designed to assist individuals and organizations in navigating the complex process of fraud reporting, investigation, and fund recovery. We combine cutting-edge technology with expert legal support to provide a robust solution for victims of crypto theft.
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Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
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1. TIME SERIES ANALYSIS
Modeling and Forecasting
Presented by
Vaibhav Jain (A13021)
Maruthi Nataraj (A13009)
Sunil Kumar (A13020)
Punit Kishore (A13011)
Arbind Kumar (A13003)
2. AGENDA
Introduction
Objective
Data Preparation
Check for Volatility
Check for Non-Stationarity
Check for Seasonality
Model Identification and Estimation
Forecasting
Graphical Forecast
3. INTRODUCTION
Time Series relates to values taken by a variable over time (such as daily sales
revenue, weekly orders, monthly overheads, yearly income) and tabulated or
plotted as chronologically ordered numbers or data points to yield valid
statistical inferences.
Trend : A Time series data may show steady upward trend or downward
movement with little fluctuation for a period of years and this may be due to
factors like increase in population, change in technological progress ,large scale
shift in consumers demands etc.
Seasonal variations: They are short-term fluctuation in a time series which
occur periodically within a year. The major factors that are responsible for the
repetitive pattern of seasonal variations are weather conditions and customs of
people. Each year more ice creams are sold in summer and very little in Winter
season.
4. INTRODUCTION
Cyclical variations : They are recurrent
upward or downward movements in a time
series but the period of cycle is greater than
a year.
Irregular variations : They are
fluctuations in time series that are short in
duration, erratic in nature and following no
regularity in the occurrence(random).
5. OBJECTIVE
The two main objectives of Time Series Analysis are :
To understand the underlying structure of Time Series represented by sequence
of observations by breaking it down to its components.
To fit a mathematical model and proceed to forecast the future.
In this study, we have to project the sales for the next 12 months .
The dataset used here is SASHELP.AIR which is Airline data and contains
two variables – DATE and AIR( labeled as International Airline Travel).
It contains the data from JAN 1949 to DEC 1960.
6. DATA PREPARATION
Check for Volatility
The plot of the data with time on horizontal axis and time series on
vertical axis provides an indication for volatility.
A fan shaped or an inverted fan shaped plot shows high volatility.
For fan shaped plot, ‘log’ or ‘square root’ transformation is used to
reduce volatility ,while for inverted fan shaped plot ,’ exponential’ or
‘square’ transformation is used.
(AIR data has been copied to MAIR dataset)
9. DATA PREPARATION
Check for Volatility
After log transformation ,with reduced volatility (constant variance)
10. DATA PREPARATION
Check for Non-Stationarity
If the data is completely random with no fixed pattern, it is called non-
stationary data and cannot be used for future forecasting. This is checked by
‘Augmented Dickey-Fuller Unit Root Test’ (ADF).Here,
H0 : Data is non-stationary
If p < alpha, we reject H0 to claim that the data is stationary and hence
can be used for forecasting.
If p > alpha, we get non-stationary data which can be converted to
stationary by successive differencing.
We can start with first difference (y[t]-y[t-1]) which can obtained using
DIF(L_AIR) or L_AIR(1).Similarly, if we need second difference, it is
DIF2(L_AIR) .
12. DATA PREPARATION
Check for Non-Stationarity
Non stationary data is
converted into stationary by
first differencing.
13. DATA PREPARATION
Check for Seasonality
The Auto Correlation function (ACF) gives the correlation between y[t]-y[t-s]
where ‘s’ is the period of lag.
If the ACF gives high values at fixed interval, that interval can be considered as the
period of seasonality. A differencing of same order will deseasonalize the data.
In the previous output of ACF as shown below, we can see that 12 years is period of
seasonality.
14. Here, we have deseasonalized data by 12th order differencing as
shown above.
DATA PREPARATION
Check for Seasonality
15. MODEL IDENTIFICATION
AND ESTIMATION
Depending upon the number of future time points to be forecasted, we set
aside few of the most recent time points data as the validation sample(V). The
rest of the data which is the development sample(D), is used to generate forecasts
for different models.
MINIC (Minimum Information Criteria) option under PROC ARIMA generates
the minimum BIC (Bayesian Information Criteria) Model after exploring all the
possible combinations of ‘p’ (Auto Regressive) and ‘q’ (Moving Average) lags
from 0 to 5 (default).
16. MODEL IDENTIFICATION
AND ESTIMATION
By observation, we can see that the minimum of the matrix is the value -6.3503
corresponding to AR 3 and MA 0 location(i.e. p=3 & q=0).
We consider all the models in the neighborhood of this model and for each of
them generate AIC (Akaike Information Criteria) and SBC (Schwartz Bayesian
Criteria) and calculate the average of them.
We select the top 6-7 models based on relatively lower value of the average
and for each of them generate forecasts.
p=0 , q=1
18. FORECASTING
Forecasts are generated using the FORECAST option in PROC ARIMA.
Here,
LEAD = No of future time points to forecast
ID = Name of time variable
INTERVAL = Unit of time variable
OUT = Name of the output file which saves the forecast
The forecasts generated (for 1960 in this case) for each combination selected
from AIC & SBC are separately compared with the actual values of the same time
point stored in the validation dataset (V) and ‘MAPE’ (Mean Absolute Percentage
Error) is calculated.
20. FORECASTING
We select the combination
(p, q) which has the
minimum MAPE and that
model is applied on the
entire data to generate the
final forecast (for 1961).
Here, we need to
apply Antilog(exp) to
get back original data
for convenience in
comparison.