Time Series Forecasting (6): Temporal Convolutional Networks (TCN)

Fri, 15 Nov 2024 09:00:00 +0000

For most of the 2010s, saying “deep learning for time series” meant using LSTM. The story changed in 2018 when Bai, Kolter, and Koltun published An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling. Their result was surprisingly simple: use a stack of 1-D convolutions, make them causal (no peeking at the future), space the filter taps exponentially (dilation), wrap the whole thing in residual connections, and train. Task after task, the resulting Temporal Convolutional Network (TCN) matched or beat LSTM/GRU — while training several times faster because every time step in the forward pass runs in parallel.

TCN on Chen Kai Blog

Time Series Forecasting (6): Temporal Convolutional Networks (TCN)