A recurring problem I see in forecasting is that most resources stop at surface-level recipes: ARIMA folklore, shallow ML pipelines, or Kaggle tricks that break in production. I spent the last years writing a book that goes much deeper and treats forecasting as a modern probabilistic inference problem, not a curve-fitting exercise. What it focuses on (and what most books don’t): Forecasting ≠ point prediction: uncertainty, distributional forecasts, and failure modes Why many “accurate” models are structurally wrong Time series as dependent data (not i.i.d. with a time index glued on) When classical methods fail — and when they still beat deep learning Modern ML (boosting, probabilistic models) used correctly Evaluation beyond RMSE: coverage, calibration, stability Real-world constraints: regime change, feedback loops, limited data No fluff, no motivational filler, no cargo-cult deep learning. It’s written for people who actually deploy forecasts and get blamed when they fail. I also released a full video walkthrough of Chapter 1, so people can judge the depth before buying. Links: Standard edition: https://valeman.gumroad.com/l/MasteringModernTimeSeriesForec... Pro edition (extras, updates): https://valeman.gumroad.com/l/MasteringModernTimeSeriesForec... Genuinely interested in feedback from HN folks who’ve built forecasting systems in anger — especially where you think the field still gets things wrong.