Modern Time Series Forecasting with Python
Build real-world time series forecasting systems which scale to millions of time series by applying modern machine learning and deep learning concepts
Key Features- Explore industry-tested machine learning techniques used to forecast millions of time series
- Get started with the revolutionary paradigm of global forecasting models
- Get to grips with new concepts by applying them to real-world datasets of energy forecasting
- Find out how to manipulate and visualize time series data like a pro
- Set strong baselines with popular models such as ARIMA
- Discover how time series forecasting can be cast as regression
- Engineer features for machine learning models for forecasting
- Explore the exciting world of ensembling and stacking models
- Get to grips with the global forecasting paradigm
- Understand and apply state-of-the-art DL models such as N-BEATS and Autoformer
- Explore multi-step forecasting and cross-validation strategies
The book is for data scientists, data analysts, machine learning engineers, and Python developers who want to build industry-ready time series models. Since the book explains most concepts from the ground up, basic proficiency in Python is all you need. Prior understanding of machine learning or forecasting will help speed up your learning. For experienced machine learning and forecasting practitioners, this book has a lot to offer in terms of advanced techniques and traversing the latest research frontiers in time series forecasting.
Auteur | | Manu Joseph |
Taal | | Engels |
Type | | Ebook |
Categorie | | Computers & Informatica |