Modern Time Series Forecasting with Python: 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 with Python, including global ML models, conformal prediction, and transformer architectures

Key Features

• Work through examples of how to use machine learning and global machine learning models for forecasting
• Enhance your time series toolkit by using deep learning models, including RNNs, transformers, and N-BEATS
• Learn probabilistic forecasting with conformal prediction 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. With Modern Time Series Forecasting with Python, Second Edition, you'll master cutting-edge deep learning architectures and advanced statistical techniques alongside classic methods like ARIMA and exponential smoothing. Learn the fundamentals from preprocessing, feature engineering, and evaluation to applying powerful machine and deep learning models, including ensemble and global methods.

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, quantitative analysts, financial analysts, meteorologists, risk analysts, and anyone interested in leveraging Python for accurate time series forecasting.

Table of Contents

1. Introducing Time Series
2. Acquiring and Processing Time Series Data
3. Analyzing and Visualizing Time Series Data
4. Setting a Strong Baseline Forecast
5. Time Series Forecasting as Regression
6. Feature Engineering for Time Series Forecasting
7. Target Transformations for Time Series Forecasting
8. Forecasting Time Series with Machine Learning Models
9. Ensembling and Stacking
10. Global Forecasting Models
11. Introduction to Deep Learning
12. Building Blocks of Deep Learning for Time Series
13. Common Modeling Patterns for Time Series
14. Attention and Transformers for Time Series
15. Strategies for Global Deep Learning Forecasting Models
16. Specialized Deep Learning Architectures for Forecasting
17. Probabilistic Forecasting and Other Use Cases
18. Multi-Step Forecasting
19. Evaluating Forecasts – Forecast Metrics
20. Evaluating Forecasts – Validation Strategies