E-Books →Predictive Time Series Forecasting with Python Mastering Machine Learning and Deep Learning Approaches for Industry-Ready

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.