Modern Time Series Forecasting with Python: Explore industry-ready time series forecasting using modern machine learning and deep learning - Paperback
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Hands-on ML and deep learning model implementations for time series forecasting enable practical learning and application
Covers both classical statistical methods and advanced global forecasting models for a comprehensive approach
Explains and applies state-of-the-art models such as N-Beats and Autoformer for cutting-edge forecasting
Includes techniques for multi-step forecasting, cross-validation, and forecast metric analysis to solve complex problems
Focuses on real-world datasets and practical topics like interpretability and ensembling to bridge theory and practice
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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