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Using Multiple Decomposition Methods and Combining a Multitude of Experts

Setu Gupta

Abstract


It has been observed over a period of time that the task of selecting a model to produce forecasts over some time series has always been a difficult one. The two major schools of thought are decomposition and combining. In decomposition, the time series is decomposed into its constituents - trend, seasonality and irregularity. Each of these components is forecasted separately using different experts. Then these forecasted components are recomposed to get the final forecast. In combining, many experts are used for forecasting the time series. Forecast from each of the expert is then combined using either mean or the median of all the experts to get the final forecast. But it has also been observed that both these approaches can be combined to get better forecasts.

We study the effect of the change of the definition of trend on the forecasts. We slowly move from no-look-ahead-trend to center-trend and observe the performance of the forecasts thus obtained. We use both, decomposition and combining, in all our experiments.








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