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IDEA: Interpretable Dynamic Ensemble Architecture for Time Series Prediction. (arXiv:2201.05336v1 [cs.LG])
Jan. 17, 2022, 2:10 a.m. | Mengyue Zha, Kani Chen, Tong Zhang
cs.LG updates on arXiv.org arxiv.org
We enhance the accuracy and generalization of univariate time series point
prediction by an explainable ensemble on the fly. We propose an Interpretable
Dynamic Ensemble Architecture (IDEA), in which interpretable base learners give
predictions independently with sparse communication as a group. The model is
composed of several sequentially stacked groups connected by group backcast
residuals and recurrent input competition. Ensemble driven by end-to-end
training both horizontally and vertically brings state-of-the-art (SOTA)
performances. Forecast accuracy improves by 2.6% over the best …
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