Improving time series forecasting: An approach combining bootstrap aggregation, clusters and exponential smoothing
| Autor: | Fernando Luiz Cyrino Oliveira, Tiago Mendes Dantas |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Computer science
Bootstrap aggregating Exponential smoothing 02 engineering and technology Variance (accounting) Covariance 01 natural sciences Medoid 010104 statistics & probability Statistics 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Variance reduction 0101 mathematics Business and International Management Time series Cluster analysis |
| Zdroj: | International Journal of Forecasting. 34:748-761 |
| ISSN: | 0169-2070 |
| DOI: | 10.1016/j.ijforecast.2018.05.006 |
| Popis: | Some recent papers have demonstrated that combining bagging (bootstrap aggregating) with exponential smoothing methods can produce highly accurate forecasts and improve the forecast accuracy relative to traditional methods. We therefore propose a new approach that combines the bagging, exponential smoothing and clustering methods. The existing methods use bagging to generate and aggregate groups of forecasts in order to reduce the variance. However, none of them consider the effect of covariance among the group of forecasts, even though it could have a dramatic impact on the variance of the group, and therefore on the forecast accuracy. The proposed approach, referred to here as Bagged.Cluster.ETS, aims to reduce the covariance effect by using partitioning around medoids (PAM) to produce clusters of similar forecasts, then selecting several forecasts from each cluster to create a group with a reduced variance. This approach was tested on various different time series sets from the M3 and CIF 2016 competitions. The empirical results have shown a substantial reduction in the forecast error, considering sMAPE and MASE. |
| Databáze: | OpenAIRE |
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