Abstract:
Machine learning algorithms dedicated to time series forecasting have gained a lot of interest over the last few years. One difficulty lies in the choice between several algorithms, as their estimation accuracy may be unstable over time. Online Aggregation of Experts combines a finite set of forecasting models without making assumptions about the data. The mixture is updated continuously when data becomes available. This is a desirable feature in non-stationary environments as it allows to reconsider at each time step what are the best estimators.
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