Time Series
5articles in this topic.
Evaluating forecasts: MAE, RMSE, MAPE, and honest backtesting
A practical guide to forecast accuracy using MAE RMSE MAPE and MASE, plus rolling-origin backtesting to avoid self-deception in time series.
The forecasting baselines that quietly beat fancy models
Why forecasting baselines like the naive forecast and seasonal-naive outperform complex models—and how to pick the right one before you build anything fancy.
Stationarity, differencing, and why ARIMA needs a flat series
Understand stationarity, why differencing transforms a trending series, and how the d in ARIMA(p,d,q) bridges raw data to a forecastable model.
Decomposition: reading trend, seasonality, and residual
A practical guide to time series decomposition — separating trend, seasonality, and residual to reveal what a signal is actually doing.
Why you can't shuffle a time series: splits and leakage
Random k-fold on a time series leaks the future into training. Learn the correct time-based train test split, rolling-origin CV, and how to avoid leakage.