Apple Academic Press

Time Series and Multivariate Models
Its Applications Using R

Editors: Pradeep Mishra, PhD
Soumik Ray, PhD
Aynur Yonar, PhD
Fozia Homa, PhD

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A strong tool for understanding, interpreting, and forecasting data gathered over time is time series analysis. The capacity to interpret time series data successfully is essential for making educated judgments in a variety of sectors, including environmental science, finance, economics, and many others, in today’s data-driven world. This new book, Time Series and Multivariate Models: Its Application using R, offers a thorough introduction to the theory and real-world uses of multivariate modeling and time series analysis with the R programming language.

An introduction to time series analysis is given at the start of the book, outlining the basic ideas and methods involved in the analysis of time-dependent data. After that, it explores more complex subjects, such as the widely used Exponential Smoothing Model (ETS) and the Auto Regressive Integrated Moving Average (ARIMA) model for time series forecasting. The book also explains the artificial neural network models and how they are used in time series analysis, giving readers a useful grasp of these potent instruments. The use of deep learning algorithms—more especially, Long Short-Term Memory (LSTM) networks—for forecasting and categorization of time series data is also covered in the book.

Apart from time series analysis, this book also includes multivariate modeling approaches such as panel regression, structural break analysis, and the Vector Autoregression Moving Average (VARMA) model. Analyzing complicated datasets with several connected variables is where these strategies are really helpful. Additionally, the book uses examples from the association of energy and agricultural price indexes to explain the application of asymmetric analysis in co-integration and causation relationships. This gives readers a better understanding of how to use these methods in practical situations. Along with covering covariance analysis in agricultural data analysis, the book also covers the fundamentals of multivariate analysis and how to apply it with R. It also gives users a thorough toolkit for studying complicated datasets by introducing nonparametric modeling approaches and how to apply them with R.

The final section of the book discusses the use of logistic regression in the analysis of agricultural data, emphasizing the value of this method in the prediction of binary outcomes based on input factors. Throughout the book’s preparation, the needs of the practitioners, researchers, teachers, and students in agricultureand related subjects remained the main focus.

CONTENTS:
Preface

1. Introduction to Time Series Analysis
C. A. Uzake

2. Auto Regressive Integrated Moving Average (ARIMA) Model
Aynur Yonur and Harun Yonor

3. Exponential Smoothing Model (ETS)
Oznur Ozaltin and Aynur Yonar

4. The Ultimate Guide to SARIMA Modeling: A Comprehensive Tutorial with Applications and Best Practices
Abdullah Mohammad Ghazi Al Khatib and Bayan Mohamad Alshaib

5. Classification and Forecasting with Deep Learning Algorithms (LSTM) in Time Series Data
Tufleuddin Biswas, Soumik Ray, Pradeep Mishra, Abhrajyoti Dalal, and Subharajyoti Dalal

6. A Comprehensive Guide to ARDL Bounds Testing: Theory, Applications, and Best Practices
Abdullah Mohammad Ghazi Al khatib and Bayan Mohamad Alshaib

7. Artificial Neural Network Models and Application using R
Claris Shoko

8. An Application of VARMA (Vector Autoregression Moving Average) Model
Soumik Ray, Tufleuddin Biswas, Pradeep Mishra, Banjul Bhattacharyya, and Saddam Hossen Majumder

9. Panel Regression and Structural Break Analysis Application in Agriculture
Ritu Rathore, Amit Thakur, Priyanka Lal, and Fozia Homa

10. Asymmetric Analysis in Co-Integration and Causality Relationships: Example on Energy and Agriculture Price Indexes Association
Hicham Ayad

11. Introduction to Multivariate Analysis and Its Application with R
Sandip Shil, Ananta Sarkar, Chandran KP, Soumen Pal, and Arun Kumar Sit

12. Analysis of Covariance in Agricultural Data Analysis
Sandip Shil, Chandran KP, Ananta Sarkar, Soumen Pal, and Arun Kumar Sit

13. Introduction to Nonparametric Modeling Techniques and Its Application with R
Sandip Shil, C. T. Jose, Chandran KP, Muralidharan K., Soumen Pal, and Arun Kumar Sit

14. Application of Logistic Regression in Agricultural Data Analysis
Ananta Sarkar, Sandip Shil, and Soumen Pal

15. An Overview of Discriminant Analysis for Classification and Forecasting
S. R. Krishna Priya and N. Naranammal

Index

About the Authors / Editors:
Editors: Pradeep Mishra, PhD
Assistant Professor of Statistics, College of Agriculture, Rewa, Jawaharlal Nehru Krishi Vishwa Vidyalaya (JNKVV), Madhya Pradesh, India

Pradeep Mishra, PhD, is an Assistant Professor of Statistics at the College of Agriculture at Jawaharlal Nehru Krishi Vishwa Vidyalaya (JNKVV), Madhya Pradesh, India. He has served as a data management specialist at a private multinational company more than three years. He has published more than 160 research papers in international and national journals. He was named a Young Scientist at the International Conference of Global Research Initiatives for Sustainable Agriculture And Allied Sciences (GRISAAS–2017). He received a best doctoral degree award at the International Conference on Agricultural and Allied Science Technologies (ICAAST–2018) and a second best paper from the Society of Economics and Development at Punjab Agricultural University (PAU) in 2018, among other awards. He specializes in the field of time series and design of experimental and agriculture statistics. Dr. Mishra completed his PhD in Agriculture Statistics, with specialization in Agricultural Statistics, Econometrics and Forecasting at Bidhan Chandra Krishi Viswavidyalaya (BCKV), Kalyani, West Bengal, India.

Soumik Ray, PhD
Assistant Professor and Head, Department of Agricultural Economics and Statistics, Centurion University of Technology and Management, Odisha, India

Soumik Ray, PhD, is an Assistant Professor and Head of the Department of Agricultural Economics and Statistics at Centurion University of Technology and Management, Odisha, India. He has been recognized with a NCR TNF BASE-2020 Young Scientist Award in Agra, India, and the BASE Young Scientist Award during an international conference organized by BASE (Biology, Agriculture, SciTech, and Agriculture Congress Association) (ICEGTABPS-2021) in India. Dr. Ray is a life member of the Society of Economic and Development at Punjab Agricultural University (PAU), India, and the Society for the Application of Statistics in Agriculture and Allied Sciences at Bidhan Chandra Krishi Viswavidyalaya (BCKV), India. He has authored 42 research papers published in prestigious national and international journals. Dr. Ray’s research interests encompass time series analysis, statistical modeling and forecasting, applied econometrics, machine learning, and deep learning models. Dr. Ray completed his MSc and PhD degrees with a National Fellowship in Agricultural Statistics from BCKV, West Bengal, India.

Aynur Yonar, PhD
Assistant Professor, Department of Statistics, University of Selçuk, Konya, Turkey

Aynur Yonar, PhD, is an Assistant Professor in the Department of Statistics at the University of Selçuk, Konya, Turkey. She earned her BS (Statistics) degree as the top student of her department from Ankara University, Turkey. She received her MS (2016) and PhD.(Statistics) (2020) degrees from Selçuk University, Turkey. Her research interests include artificial intelligence, machine learning, multicriteria decision-making, and fuzzy, time series, estimation, prediction, and forecasting. She has published around 25 research articles and seven book chapters.

Fozia Homa, PhD
Assistant Professor and Scientist, Department of Statistics, Mathematics and Computer Application, Bihar Agricultural University, Sabour, India

Fozia Homa, PhD, is an Assistant Professor and Scientist in the Department of Statistics, Mathematics and Computer Application at Bihar Agricultural University, Sabour, India. She is also the author or co-author of several journal articles. Dr Fozia has received numerous awards in recognition of research and teaching achievements from several organizations of national and international repute. She is conferred with the Young Scientist Award in the field of Mathematics and Statistics–2016 by AUFAU, Salem, Tamil Nadu, India. She was also awarded with SP Dhall Distinguished Publication Awards in Statistics 2015 by the Society for Advancement of Human and Nature, Himachal Pradesh, India; Young Scientist Award–2015 by Venus International Foundation, Chennai, India, Best Young Researcher Award-2015 by GRABS Educational Trust, Chennai, India. She has been an active member of organizing committees of several national and international seminars/conferences/summits. She has received several grants from various funding agencies to carry out her research projects. She is dynamically indulged in teaching (graduate, postgraduate and doctorate students) and research, and she has proved herself as an active scientist in the area of sample surveys, population studies, and mathematical modelling. Dr. Homa acquired a BSc (Statistics Hon’s) and MSc (Statistics) degree from Banaras Hindu, University, Varanasi, Utter Pradesh, India and PhD.(Applied Statistics) with specialization in Sampling Techniques from the Indian Institute of Technology (Indian School of Mines), Dhanbad, Jharkhand, India.

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