Classical and deep learning methods for temporal data
Components, stationarity, autocorrelation, and proper train/test splitting
ARIMA, SARIMA, exponential smoothing, and Prophet
LSTM, GRU, sequence-to-sequence, and temporal convolutional networks
Transformer-based models, N-BEATS, foundation models, and probabilistic forecasting