تقرير
Sparsification and Filtering for Spatial-temporal GNN in Multivariate Time-series
| العنوان: | Sparsification and Filtering for Spatial-temporal GNN in Multivariate Time-series |
|---|---|
| المؤلفون: | Wang, Yuanrong, Aste, Tomaso |
| سنة النشر: | 2022 |
| المجموعة: | Computer Science Quantitative Finance |
| مصطلحات موضوعية: | Computer Science - Machine Learning, Quantitative Finance - Computational Finance |
| الوصف: | We propose an end-to-end architecture for multivariate time-series prediction that integrates a spatial-temporal graph neural network with a matrix filtering module. This module generates filtered (inverse) correlation graphs from multivariate time series before inputting them into a GNN. In contrast with existing sparsification methods adopted in graph neural network, our model explicitly leverage time-series filtering to overcome the low signal-to-noise ratio typical of complex systems data. We present a set of experiments, where we predict future sales from a synthetic time-series sales dataset. The proposed spatial-temporal graph neural network displays superior performances with respect to baseline approaches, with no graphical information, and with fully connected, disconnected graphs and unfiltered graphs. Comment: 7 pages, 1 figure, 3tables |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2203.03991 |
| رقم الأكسشن: | edsarx.2203.03991 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |