Dealing with seasonality by narrowing the training set in time series forecasting with kNN

•A new scheme for dealing with seasonality in time series forecasting.•It is based on building a different model to forecast every season.•Experimental results with nearest neighbor regression enhance classical approaches. In this paper, a new strategy for dealing with time series exhibiting a seasonal pattern is proposed. The strategy is applied in the context of time series forecasting using kNN regression. The key idea is to forecast every different season using a different specialized kNN learner. Each learner is specialized because its training set only contains examples whose targets belong to the season that is able to forecast. This way, the forecast of a specialized kNN learner is an aggregation of target values of the same season, reducing the likelihood of misleading forecasts. Although the strategy is applied to kNN, we think that other computational intelligence approaches could take advantage of it.