Modern Time Series Forecasting with Python, 2nd Edition

eBook Details:

• Paperback: 658 pages
• Publisher: WOW! eBook; 2nd edition (October 31, 2024)
• Language: English
• ISBN-10: 1835883184
• ISBN-13: 978-1835883181

eBook Description:

Modern Time Series Forecasting with Python, 2nd Edition: Industry-ready machine learning and deep learning time series analysis with PyTorch and pandas. 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.

Predicting the future, whether it’s market trends, energy demand, or website traffic, has never been more crucial. This practical, hands-on Modern Time Series Forecasting with Python, 2nd Edition 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 Modern Time Series Forecasting with Python, Second Edition 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.

• 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

This new Modern Time Series Forecasting with Python, 2nd 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.