Modern Time Series Forecasting with Python - Second Edition Industry-ready machine learning and deep learning time series analysis with PyTorch and pandas
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- Englisch ausgewählt
Fr. 89.90
inkl. gesetzl. MwSt.,
Beschreibung
Produktdetails
Einband
Taschenbuch
Erscheinungsdatum
31.10.2024
Verlag
Packt PublishingSeitenzahl
660
Maße (L/B/H)
23.5/19.1/3.6 cm
Gewicht
1212 g
Auflage
2. Auflage
Sprache
Englisch
ISBN
978-1-83588-318-1
Learn traditional and cutting-edge machine learning (ML) and deep learning techniques and best practices for time series forecasting, including global forecasting models, conformal prediction, and transformer architectures Key Features: - Apply ML and global models to improve forecasting accuracy through practical examples - Enhance your time series toolkit by using deep learning models, including RNNs, transformers, and N-BEATS - Learn probabilistic forecasting with conformal prediction, Monte Carlo dropout, and quantile regressions - Purchase of the print or Kindle book includes a free eBook in PDF format Book Description: Predicting the future, whether it's market trends, energy demand, or website traffic, has never been more crucial. This practical, hands-on guide empowers you to build and deploy powerful time series forecasting models. Whether you're working with traditional statistical methods or cutting-edge deep learning architectures, this book provides structured learning and best practices for both. Starting with the basics, this data science book introduces fundamental time series concepts, such as ARIMA and exponential smoothing, before gradually progressing to advanced topics, such as machine learning for time series, deep neural networks, and transformers. As part of your fundamentals training, you'll learn preprocessing, feature engineering, and model evaluation. As you progress, you'll also explore global forecasting models, ensemble methods, and probabilistic forecasting techniques. This new edition goes deeper into transformer architectures and probabilistic forecasting, including new content on the latest time series models, conformal prediction, and hierarchical forecasting. Whether you seek advanced deep learning insights or specialized architecture implementations, this edition provides practical strategies and new content to elevate your forecasting skills. What You Will Learn: - Build machine learning models for regression-based time series forecasting - Apply powerful feature engineering techniques to enhance prediction accuracy - Tackle common challenges like non-stationarity and seasonality - Combine multiple forecasts using ensembling and stacking for superior results - Explore cutting-edge advancements in probabilistic forecasting and handle intermittent or sparse time series - Evaluate and validate your forecasts using best practices and statistical metrics Who this book is for: This book is ideal for data scientists, financial analysts, quantitative analysts, machine learning engineers, and researchers who need to model time-dependent data across industries, such as finance, energy, meteorology, risk analysis, and retail. Whether you are a professional looking to apply cutting-edge models to real-world problems or a student aiming to build a strong foundation in time series analysis and forecasting, this book will provide the tools and techniques you need. Familiarity with Python and basic machine learning concepts is recommended. Table of Contents - Introducing Time Series - Acquiring and Processing Time Series Data - Analyzing and Visualizing Time Series Data - Setting a Strong Baseline Forecast - Time Series Forecasting as Regression - Feature Engineering for Time Series Forecasting - Target Transformations for Time Series Forecasting - Forecasting Time Series with Machine Learning Models - Ensembling and Stacking - Global Forecasting Models - Introduction to Deep Learning - Building Blocks of Deep Learning for Time Series - Common Modeling Patterns for Time Series - Attention and Transformers for Time Series (N.B. Please use the Read Sample option to see further chapters)
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