Benchmarking Attention-Based Interpretability of Deep Learning in Multivariate Time Series Predictions
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Externally hosted supplementary file 1
Link: https://github.com/hc-xai/mts-interpretability-benchmark
Description: Code for generating synthetic multi-variate time series datasets of new benchmark for attention-based interpretability of deep learning in multivariate trime series forecasting tasks.
Barić, D.; Fumić, P.; Horvatić, D.; Lipic, T. Benchmarking Attention-Based Interpretability of Deep Learning in Multivariate Time Series Predictions. Entropy 2021, 23, 143. https://doi.org/10.3390/e23020143
Barić D, Fumić P, Horvatić D, Lipic T. Benchmarking Attention-Based Interpretability of Deep Learning in Multivariate Time Series Predictions. Entropy. 2021; 23(2):143. https://doi.org/10.3390/e23020143
Chicago/Turabian StyleBarić, Domjan, Petar Fumić, Davor Horvatić, and Tomislav Lipic. 2021. "Benchmarking Attention-Based Interpretability of Deep Learning in Multivariate Time Series Predictions" Entropy 23, no. 2: 143. https://doi.org/10.3390/e23020143