This book is an effort to integrate statistical theory with practical implementation. While emphasizing the fundamental concepts of time series analysis, it also provides step-by-step illustrations using Python programming. The aim is to make the subject both conceptually clear and practically relevant for students, researchers, and practitioners. Designed primarily for undergraduate and postgraduate students of statistics, mathematics, economics, and computer science, this book also serves as a reference for faculty members, professionals, and data analysts who seek to apply forecasting techniques in their respective fields. Topics such as stationarity, ARIMA models, exponential smoothing, decomposition, and advanced machine learning approaches are discussed systematically, supported by examples and applications.
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Dr. S. A. Jyothi Rani is a Professor of Statistics in the Department of Statistics, University College of Science, Osmania University, Hyderabad. She holds a PhD in Statistics (2009) and secured First Rank with Distinction in her M.Sc. (Statistics), 1998, from Osmania University. She has 20 years of teaching and 13 years of research experience.
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Paperback. Zustand: new. Paperback. This book is an effort to integrate statistical theory with practical implementation. While emphasizing the fundamental concepts of time series analysis, it also provides step-by-step illustrations using Python programming. The aim is to make the subject both conceptually clear and practically relevant for students, researchers, and practitioners. Designed primarily for undergraduate and postgraduate students of statistics, mathematics, economics, and computer science, this book also serves as a reference for faculty members, professionals, and data analysts who seek to apply forecasting techniques in their respective fields. Topics such as stationarity, ARIMA models, exponential smoothing, decomposition, and advanced machine learning approaches are discussed systematically, supported by examples and applications. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Bestandsnummer des Verkäufers 9786630008944
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Paperback. Zustand: new. Paperback. This book is an effort to integrate statistical theory with practical implementation. While emphasizing the fundamental concepts of time series analysis, it also provides step-by-step illustrations using Python programming. The aim is to make the subject both conceptually clear and practically relevant for students, researchers, and practitioners. Designed primarily for undergraduate and postgraduate students of statistics, mathematics, economics, and computer science, this book also serves as a reference for faculty members, professionals, and data analysts who seek to apply forecasting techniques in their respective fields. Topics such as stationarity, ARIMA models, exponential smoothing, decomposition, and advanced machine learning approaches are discussed systematically, supported by examples and applications. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Bestandsnummer des Verkäufers 9786630008944
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