Arbitrated Dynamic Ensemble with Abstaining for Time-Series Forecasting on Data Streams

A well-known challenge in mining temporal data streams is their dynamic nature where changes and recurrent concepts are likely to happen. Ensemble methods are powerful techniques to improve overall accuracy and tackle the aforementioned challenges by combining several classifiers. Dynamic Ensemble Selection allows selecting, on the fly, the most accurate classifiers only to contribute to the final output. This is motivated by the assumption that components of the ensemble have different degrees of expertise on different sub-spaces of the data. Existing Dynamic Ensemble Selection methods are tailored to batch learning or classification tasks but less suited to stream mining and forecasting tasks. In this paper, we propose a new Arbitrated Dynamic Ensemble Selection technique STREAMING-ADE for time-series forecasting on data streams that uses meta-learning to monitor the predictive power of ensemble components and accordingly select and weight experts. Our selection is based on an abstaining policy where poorly performing classifiers are excluded from the experts' committee. Our contribution is twofold: (i) We introduce two different approaches of abstaining: threshold-based and random-based selection and (ii) We conduct an extensive experimental study to compare different methods on both real and synthetic time-series.