Isbn: 9789349887664 - mathematics of time series forecasting: build robust time series forecasting systems in python using mathematical theory, statistical modeling, machine lea...

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Where Mathematical Rigor Meets the Art of Predicting the FutureBook DescriptionTime 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.Thus, whether you are forecasting financial markets, demand patterns, sensor data, or macroeconomic indicators, this book equips you with the mathematical insight and practical tools to build accurate, reliable, and interpretable forecasting systems.What you will learn¿ Build mathematical intuition behind ARIMA, SARIMA, VAR, and LSTM models¿ Test, transform, and prepare real-world time series for forecasting¿ Apply statistical, ML, and DL methods with Python step-by-step¿ Diagnose stationarity, seasonality, and stochastic behavior in data¿ Model multivariate time series and interpret cross-variable dependencies¿ Bridge mathematical theory with applied forecasting across domainsWho is This Book For 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.Table of Contents1. Introduction to Time Series and Mathematical Foundations2. Preparing Time Series Data3. Tests for Stationarity - Part 14. Tests for Stationarity - Part 25. Tests for Stationarity - Part 36. Foundations of Time Series Preparation7. Statistical Models for Forecasting8. ML and DL for Timeseries9. Multivariate Time Series ModelsIndex.…

Mathematics of Time Series Forecasting

Sulekha Aloorravi

Idioma: Inglés

Editorial: Orange Education Pvt Ltd, 2026

9349887665 / 9789349887664

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Impresión bajo demanda

Librería: preigu, Osnabrück, Alemaniapreigu

Vendedor de 5 estrellas

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Condición: Nuevo

EUR 47,35

Envío por EUR 70,00

Se envía de Alemania a Estados Unidos de America

Cantidad disponible: 5 disponibles

Isbn: 9789349887664 - mathematics of time series forecasting: build robust time series forecasting systems in python using mathematical theory, statistical modeling, machine learning, and deep learning (english edition) (10 resultados)

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Mathematics of Time Series Forecasting

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Mathematics of Time Series Forecasting: Build Robust Time Series Forecasting Systems in Python Using Mathematical Theory, Statistical Modeling, Machine Learning, and Deep Learning (English Edition)

Mathematics of Time Series Forecasting

Mathematics of Time Series Forecasting

Mathematics of Time Series Forecasting

Mathematics of Time Series Forecasting

Mathematics of Time Series Forecasting

Mathematics of Time Series Forecasting

Mathematics of Time Series Forecasting