Mastering Modern Time Series Forecasting – A Practical Guide in Python

Show HN: Mastering Modern Time Series Forecasting – A Practical Guide in Python

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I’ve been working on a practical guide for time series forecasting in Python, called Mastering Modern Time Series Forecasting. The goal is to bridge the gap between theory and implementation. It covers both classical statistical models (ARIMA, SARIMA, Prophet) and modern machine/deep learning approaches (N-BEATS, Transformers, Temporal Fusion Transformer). The code examples use Python libraries like statsmodels, scikit-learn, PyTorch, and Darts, and the book focuses on real-world workflows: messy data, feature engineering, model selection, and evaluation. I wrote this after struggling to find forecasting resources that were both practical and up-to-date — especially for applied ML practition

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Website: valeman.gumroad.com/l/MasteringModernTimeSeriesForec