A Study on Graph-Structured Recurrent Neural Networks and Sparsification with Application to Epidemic Forecasting
2019-02-13Code Available0· sign in to hype
Zhijian Li, Xiyang Luo, Bao Wang, Andrea L. Bertozzi, Jack Xin
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Abstract
We study epidemic forecasting on real-world health data by a graph-structured recurrent neural network (GSRNN). We achieve state-of-the-art forecasting accuracy on the benchmark CDC dataset. To improve model efficiency, we sparsify the network weights via transformed-_1 penalty and maintain prediction accuracy at the same level with 70% of the network weights being zero.