Mathematics of Time Series Forecasting by Sulekha AloorRavi (.ePUB)+
File Size: 13.0 MB
Mathematics of Time Series Forecasting: Build Robust Time Series Forecasting Systems in Python Using Mathematical Theory, Statistical Modeling, Machine Learning, and Deep Learning by Sulekha AloorRavi
Requirements: .ePUB, .PDF reader, 13.0 MB | PDF, True EPUB
Overview: Where Mathematical Rigor Meets the Art of Predicting the Future. Time series forecasting is one of the most valuable skills an AI/ML professional can possess. Mathematics of Time Series Forecasting transforms the complexity of time-dependent data into a clear, intuitive, and powerful framework for prediction. This book bridges rigorous mathematical foundations with hands-on implementation, allowing readers to truly understand—not just apply the forecasting models. Beginning with the core principles of time series behavior, you will learn how to diagnose stationarity, seasonality, and stochastic patterns that shape real-world datasets. Step-by-step derivations guide you through the mathematics behind ARIMA, SARIMA, Exponential Smoothing, VAR, and other classical models, while practical Python examples demonstrate how these methods are built and validated in practice. The book then moves beyond traditional statistics, exploring machine learning and deep learning techniques—including gradient boosting, neural networks, and LSTMs—that have transformed the forecasting landscape. This book is tailored for data scientists, analysts, and engineers with a foundational understanding of statistics, linear algebra, and Python programming. Readers should also be comfortable with basic data manipulation and visualization to fully benefit from the mathematical depth and practical applications of time series forecasting.
Genre: Non-Fiction > Tech & Devices
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