Time Convolution
The temporal convolution is responsible for capturing temporal patterns in a sequence.
Dilated Temporal Convolution
Unit8 has a nice blog about temporal convolution and dilated temporal convolution^{1}. In this
Inception
A good convolutional network should capture both shortterm and longterm patterns in the time series data. However,
 single large kernel is good for longterm pattern but not good at shortterm pattern,
 single small kernel is good for shortterm pattern but not good at longterm pattern.
The inception strategy employs multiple dilated temporal convolution and the outputs are concatenated. By applying multiple such layers, we can extract patterns longer than any of the single dilated temporal convolutions^{2}.
$$ \operatorname{concat}( z\star f_2, z\star f_3, z\star f_6, z\star f_7). $$
Dilated Inception Layer
See Fig 5 in the paper^{2}.

Lässig2021 Lässig F. Temporal Convolutional Networks and Forecasting. In: Unit8 [Internet]. 6 Jul 2021 [cited 28 Nov 2022]. Available: https://unit8.com/resources/temporalconvolutionalnetworksandforecasting/ ↩︎

Wu2020 Wu Z, Pan S, Long G, Jiang J, Chang X, Zhang C. Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks. arXiv [cs.LG]. 2020. Available: http://arxiv.org/abs/2005.11650 ↩︎
 Wu2020 Wu Z, Pan S, Long G, Jiang J, Chang X, Zhang C. Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks. arXiv [cs.LG]. 2020. Available: http://arxiv.org/abs/2005.11650
 Lässig2021 Lässig F. Temporal Convolutional Networks and Forecasting. In: Unit8 [Internet]. 6 Jul 2021 [cited 28 Nov 2022]. Available: https://unit8.com/resources/temporalconvolutionalnetworksandforecasting/
L Ma (2022). 'Time Convolution', Datumorphism, 11 April. Available at: https://datumorphism.leima.is/cards/forecasting/timeconvolution/.