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 practitioners.
You can find the book here: https://valeman.gumroad.com/l/MasteringModernTimeSeriesForec...https://leanpub.com/mastering_modern_time_series_forecasting
Happy to answer questions or hear feedback from anyone working with time series forecasting or Python ML tools.
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