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Modern Time Series Forecasting with Python - Second Edition (Early Releas) - BaDshaH - 09-23-2024
![[Image: 19989fdde23876105ba493e9af980d90.jpg]](https://i124.fastpic.org/big/2024/0923/90/19989fdde23876105ba493e9af980d90.jpg)
Modern Time Series Forecasting with Python - Second Edition (Early Releas)
English | 2024 | ISBN: 9781835883181 | 181 Pages | True EPUB | 6.39 MB
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.