Why Your Forecast Fails on Christmas: Handling Multiple Seasonalities and Holiday Spikes
Learn to model overlapping seasonalities and holiday halo effects in Prophet with Fourier series and prior-scale tuning so forecasts survive the Christmas rush.
Time Series
Reference: Time Series Anatomy
A reference for time series anatomy: trend, seasonality, residual, autocorrelation, and stationarity, with Python decomposition examples and decision rules.
Time-Series Cross-Validation: Why K-Fold Breaks on Temporal Data
Learn why K-Fold causes temporal leakage on time-series data and how walk-forward validation delivers honest, production-ready R² metrics you can trust.
Prophet vs. Statistical Models: A Practical Forecasting Showdown
Compare Prophet and ARIMA head-to-head on messy real-world time series with structural breaks, holidays, and multiple seasonalities to choose the right model.
Gradient Boosting for Time Series: Using LightGBM to Forecast
Master LightGBM for time series forecasting: engineer lag and rolling features, apply time-aware validation, and prevent overfitting with regularization.
Feature Engineering for Time Series: Lags, Rolling Windows, and Seasonality
Learn how to engineer time series features like lags, rolling windows, and seasonal indicators to give your forecasting models the temporal context they need to predict accurately.
Evaluating Forecast Accuracy: MAPE, RMSE, and Why Averages Lie
Learn to evaluate forecast accuracy with MAE, RMSE, and MAPE, see why RMSE punishes big errors, and discover why a good average can hide a biased model.
Classical Forecasting vs. Machine Learning: When Does ARIMA Beat an LSTM?
Learn why ARIMA often beats LSTM on small time-series datasets, when to use classical vs. neural net forecasting, and how to avoid tuning bias in comparisons.
The 'Smart' Model that Failed
Learn why ARIMA and SARIMA outperform deep learning on small time-series datasets, with intuitive breakdowns of momentum, differencing, shocks, and seasonality.