Code for the paper "STConvS2S: Spatiotemporal Convolutional Sequence to Sequence Network for Weather Forecasting" (Neurocomputing, Elsevier)
Code for the article published in Neurocomputing
Change in the last name of the first author [Nascimento -> Castro]. I usually use my middle name on Github, Linkedin and other platforms, so it seemed right to use it also in the paper.
Code refactoring
Renaming folder and files :[experiments] -> [notebooks]; [baseline.py] -> [arima.py]; [dataset-variables.py] -> [settings.py]
Architecture nomenclature: we have rephrased the nomenclature of your proposed architecture for clarity, since your approach is not similar to the encoder-decoder architecture for 3D CNN.
Major changes (introduce new features and change the old version in incompatible ways)
We introduce a new method in our model that prevents it from violating the temporal order (STConvS2S-R). We continue using in another model (STConvS2S-C) the causal convolution (commonly used in 1D CNN), where we adapt it in spatiotemporal problems using 3D CNN.
We devise a temporal generator block that presents a new use of transposed convolutional layers to generate an output sequence whose length may be longer than the length of the input sequence.
Minor changes
Initial development, not a stable version (any changes can occur at any time).