Time Series Forecasting
Statistical and deep methods for forecasting at scale.
01Time Series Forecasting (1): Traditional Statistical Models
ARIMA, SARIMA, VAR, GARCH, Prophet, exponential smoothing and the Kalman filter, derived from a single state-space view. …
02Time Series Forecasting (2): LSTM — Gate Mechanisms and Long-Term Dependencies
How LSTM's forget, input, and output gates solve the vanishing gradient problem. Complete PyTorch code for time series …
03Time Series Forecasting (3): GRU — Lightweight Gates and Efficiency Trade-offs
GRU distills LSTM into two gates for faster training and 25% fewer parameters. Learn when GRU beats LSTM, with formulas, …
04Time Series Forecasting (4): Attention Mechanisms — Direct Long-Range Dependencies
Self-attention, multi-head attention, and positional encoding for time series. Step-by-step math, PyTorch …
05Time Series Forecasting (5): Transformer Architecture for Time Series
Transformers for time series, end to end: encoder-decoder anatomy, temporal positional encoding, the O(n^2) attention …
06Time Series Forecasting (6): Temporal Convolutional Networks (TCN)
TCNs use causal dilated convolutions for parallel training and exponential receptive fields. Complete PyTorch …
07Time Series Forecasting (7): N-BEATS — Interpretable Deep Architecture
N-BEATS combines deep learning expressiveness with classical decomposition interpretability. Basis function expansion, …
08Time Series Forecasting (8): Informer — Efficient Long-Sequence Forecasting
Informer reduces Transformer complexity from O(L^2) to O(L log L) via ProbSparse attention, distilling, and a one-shot …