UPDATE: new code released with changes in our architecture. Please see the release notes for details (Nov/2020)
This repository has the open source implementation of a new architecture termed STConvS2S. To sum up, our approach (STConvS2S) uses only 3D convolutional neural network (CNN) to tackle the sequence-to-sequence task using spatiotemporal data. We compare our results with state-of-the-art architectures. Details in the article published in Neurocomputing, Elsevier (arXiv versions).
Mainly, our code uses Python 3.6 and PyTorch 1.0. See config/environment.yml for other requirements.
To install packages with the same version as we executed our experiments, run the code below:
cd config
./create-env.sh
All datasets are publicly available at http://doi.org/10.5281/zenodo.3558773. Datasets must be placed in the data folder.
Note: In the 15-step ahead datasets, just for the convenience of having the input and targe tensors in the same file, we define them in the same shape (# of samples, 15, # of latitude, # of longitude, 1). However, in the code, we force the correct length of the input sequence in the tensor as shown here.
Jupyter notebooks for the first sequence-to-sequence task (given the previous 5 grids, predict the next 5 grids) can be found in the notebooks folder (see Table 1 and 3 in the paper).
Final experiments (see Table 2 in the paper) compare STConvS2S (our architecture) with deep learning state-of-the-art architectures and ARIMA models. We evaluate the models in two horizons: 5 and 15-steps ahead. This task is performed using main.py (for deep learning models) and arima.py (for ARIMA models). Results can be found in the output folder.
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/output/full-dataset(for deep learning models)/checkpoints: pre-trained models that allow you to recreate the training configuration (weights, loss, optimizer)./losses: training and validation losses. Can be used to recreate the error analysis plot/plots: error analysis plots from training phase/results: evaluation phase results with metric value (RMSE and MAE), training time, best epochs and so on.
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/output/arima(for ARIMA models)
First load the conda environment with the installed packages.
source activate pytorch
Below are examples of how to run each model.
We provide two variants of the STConvS2S architecture that satisfy the causal constraint. Each can be executed as follows (complete example of how we performed the exepriments, here)
python main.py -i 3 -v 4 -m stconvs2s-r --plot > output/full-dataset/results/cfsr-stconvs2s-rmse-v4.out
The above command executes STConvS2S-R (-m stconvs2s-r) in 3 iterations (-i), indicating the model version (-v), allowing the generation of plots in the training phase (--plot).
To run experiments with this architecture, switch to the -m stconvs2s-c parameter.
python arima.py > output/arima/cfsr-arima.out
- add
--chirps: change the dataset to rainfall (CHIRPS). Default dataset: temperature (CFSR). - add
-s 15: change the horizon. Default horizon: 5. - add
-l 3 -d 32 -k 5: define the number of layers (l), filters (d) and the kernel size (-k). - add
--email: send email at the end. To use this functionality, set your email in the file config/mail_config.ini. - add
--small-dataset: have a quick training using a few sample dataset.
Check out the other possible parameters here.
@article{Castro2021,
author = "Rafaela Castro and Yania M. Souto and Eduardo Ogasawara and Fabio Porto and Eduardo Bezerra",
title = "STConvS2S: Spatiotemporal Convolutional Sequence to Sequence Network for Weather Forecasting",
journal = "Neurocomputing",
volume = "426",
pages = "285 - 298",
year = "2021",
issn = "0925-2312",
doi = "https://doi.org/10.1016/j.neucom.2020.09.060"
}
To give your opinion about this work, send an email to [email protected].