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 DescriptionPredicting 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 forThis 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 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 Strategies for Global Deep Learning Forecasting Models Specialized Deep Learning Architectures for Forecasting Probabilistic Forecasting and Other Use Cases Multi-Step Forecasting Evaluating Forecasts – Forecast Metrics Evaluating Forecasts – Validation Strategies