Temporal Pattern Attention for Multivariate Time Series Forecasting
Table 2: Results on typical MTS datasets using RAE, RSE and CORR as metrics. Best performance in boldface; second best performance is underlined. We report the mean and standard deviation of our model in ten runs. All numbers besides the results of our model is referenced from the paper of LSTNet Lai et al. (2018).
['RAE Horizon', 'Solar Energy 3', 'Solar Energy 6', 'Solar Energy 12', 'Solar Energy 24', 'Traffic 3', 'Traffic 6', 'Traffic 12', 'Traffic 24']
On typical MTS datasets, we chose the best model on the validation set using RAE/RSE/CORR as the metric for the testing set. Both tables show that the proposed model outperforms almost all other methods on all datasets, horizons, and metrics. Also, our models are able to deal with a wide range of dataset size, from the smallest 534 KB Exchange Rate dataset to the largest 172 MB Solar Energy dataset. In these results, the proposed model consistently demonstrates its superiority for MTS forecasting.