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Predictive Time Series Forecasting with Python: Mastering Machine Learning and Deep Learning Approaches for Industry-Ready Predictions
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- FormatePub
- ISBN8231684960
- EAN9798231684960
- Date de parution21/05/2025
- Protection num.pas de protection
- Infos supplémentairesepub
- ÉditeurWalzone Press
Résumé
"Predictive Time Series Forecasting with Python" is a comprehensive guide that equips data scientists, analysts, and machine learning practitioners with the skills to build industry-ready forecasting models. The book bridges the gap between theoretical concepts and practical implementation, covering everything…
"Predictive Time Series Forecasting with Python" is a comprehensive guide that equips data scientists, analysts, and machine learning practitioners with the skills to build industry-ready forecasting models. The book bridges the gap between theoretical concepts and practical implementation, covering everything from classical statistical methods to cutting-edge deep learning approaches. Readers will master essential techniques for handling time-dependent data, including feature engineering, model selection, and evaluation strategies specific to temporal data.
The book explores both univariate and multivariate forecasting, probabilistic predictions, and advanced architectures like RNNs, transformers, and N-BEATS. Through hands-on examples and real-world case studies, readers will learn to build robust forecasting pipelines that can be deployed in production environments. Whether you're predicting financial markets, energy demand, retail sales, or website traffic, this book provides the tools and techniques to create accurate, scalable, and interpretable forecasting systems.
With a focus on Python implementation using libraries like pandas, PyTorch, and scikit-learn, readers will develop practical skills that can be immediately applied to solve complex forecasting challenges across industries.
The book explores both univariate and multivariate forecasting, probabilistic predictions, and advanced architectures like RNNs, transformers, and N-BEATS. Through hands-on examples and real-world case studies, readers will learn to build robust forecasting pipelines that can be deployed in production environments. Whether you're predicting financial markets, energy demand, retail sales, or website traffic, this book provides the tools and techniques to create accurate, scalable, and interpretable forecasting systems.
With a focus on Python implementation using libraries like pandas, PyTorch, and scikit-learn, readers will develop practical skills that can be immediately applied to solve complex forecasting challenges across industries.