Time Series
Time 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 generative decoder. Full math, PyTorch code, and ETT/weather benchmarks.
Time Series Forecasting (7): N-BEATS -- Interpretable Deep Architecture
N-BEATS combines deep learning expressiveness with classical decomposition interpretability. Basis function expansion, double residual stacking, and M4 competition analysis with full PyTorch code.
Time Series Forecasting (6): Temporal Convolutional Networks (TCN)
TCNs use causal dilated convolutions for parallel training and exponential receptive fields. Complete PyTorch implementation with traffic flow and sensor data case studies.
Time 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 bottleneck, decoder-only forecasting, and patching. With variants (Autoformer, FEDformer, Informer, …
Time 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 implementations, and visualization techniques for interpretable forecasting.
Time 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, benchmarks, PyTorch code, and a decision matrix.
Time 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 forecasting with practical tuning tips.
Time Series Forecasting (1): Traditional Statistical Models
ARIMA, SARIMA, VAR, GARCH, Prophet, exponential smoothing and the Kalman filter, derived from a single state-space view. With Box-Jenkins workflow, ACF/PACF identification, and runnable Python.