01 — Classical Forecasting

Statistical time series analysis: stationarity, ARIMA family, exponential smoothing, VAR, and evaluation of forecast accuracy.

Notes

• Time Series Models — stationarity, ARIMA, seasonal decomposition (STL), ETS/exponential smoothing, VAR, Kalman filter (overview), neural approaches, MAPE/RMSE evaluation
• Time Series Models — Implementation — statsmodels ARIMA/SARIMA, ETS/Prophet, evaluation metrics, backtesting framework

Links

← 05 Time Series → 02 — State-Space and Probabilistic

• Recurrent Networks (LSTM for forecasting)
• Hypothesis Testing — ADF stationarity test