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
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.