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Time Series Forecasting and Advanced Predictive Models
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189 b
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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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