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IDEA: Interpretable Dynamic Ensemble Architecture for Time Series Prediction
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 anddoi:10.48550/arxiv.2201.05336 fatcat:ayxxvkwk6rdrfgqmw2sviwow5i