| Paper ID | MLSP-29.4 |
| Paper Title |
SPATIOTEMPORAL ATTENTION FOR MULTIVARIATE TIME SERIES PREDICTION AND INTERPRETATION |
| Authors |
Tryambak Gangopadhyay, Sin Yong Tan, Iowa State University, United States; Zhanhong Jiang, Johnson Controls, United States; Rui Meng, Lawrence Berkeley National Lab, UC Berkeley, United States; Soumik Sarkar, Iowa State University, United States |
| Session | MLSP-29: Deep Learning for Time Series |
| Location | Gather.Town |
| Session Time: | Thursday, 10 June, 14:00 - 14:45 |
| Presentation Time: | Thursday, 10 June, 14:00 - 14:45 |
| Presentation |
Poster
|
| Topic |
Machine Learning for Signal Processing: [MLR-DEEP] Deep learning techniques |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
Multivariate time series modeling and prediction problems are abundant in many machine learning application domains. Accurate interpretation of the prediction outcomes from the model can significantly benefit the domain experts. In addition to isolating the important time-steps, spatial interpretation is also critical to understand the contributions of different variables on the model output. We propose a novel deep learning architecture, called spatiotemporal attention mechanism (STAM) for simultaneous learning of the most important time steps and variables. STAM is a causal (i.e., only depends on past inputs and does not use future inputs) and scalable (i.e., scales well with an increase in the number of variables) approach that is comparable to the state-of-the-art models in terms of computational tractability. We demonstrate our models' performance on a popular public dataset and a domain-specific dataset, where the learned attention weights are validated from a domain knowledge perspective. When compared with the baseline models, the results show that STAM maintains state-of-the-art prediction accuracy while offering the benefit of accurate spatiotemporal interpretability. |
