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Forecaster: A Graph Transformer for Forecasting Spatial and Time-Dependent Data
[article]
2020
arXiv
pre-print
Spatial and time-dependent data is of interest in many applications. This task is difficult due to its complex spatial dependency, long-range temporal dependency, data non-stationarity, and data heterogeneity. To address these challenges, we propose Forecaster, a graph Transformer architecture. Specifically, we start by learning the structure of the graph that parsimoniously represents the spatial dependency between the data at different locations. Based on the topology of the graph, we
arXiv:1909.04019v5
fatcat:uq63oetxgva5zo2mtmx6uv5y2e