Forecasting the NN5 time series with hybrid models

Jörg D. Wichard

International Journal of Forecasting, 2011, vol. 27, issue 3, 700-707

Abstract: We propose a simple way of predicting time series with recurring seasonal periods. Missing values of the time series are estimated and interpolated in a preprocessing step. We combine several forecasting methods by taking the weighted mean of forecasts that were generated with time-domain models which were validated on left-out parts of the time series. The hybrid model is a combination of a neural network ensemble, an ensemble of nearest trajectory models and a model for the 7-day cycle. We apply this approach to the NN5 time series competition data set.

Keywords: Forecasting; competitions; Combining; forecasts; Nonlinear; time; series; Seasonality; Neural; networks (search for similar items in EconPapers)
Date: 2011
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:27:y::i:3:p:700-707

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