A review and comparison of strategies for multi-step ahead time series forecasting based on the NN5 forecasting competition

TL;DR

This paper reviews and compares various multi-step ahead time series forecasting strategies through extensive experiments on the NN5 benchmark, highlighting the effectiveness of multiple-output methods and preprocessing techniques.

Contribution

It provides a comprehensive comparison of forecasting strategies, including the effects of deseasonalization and input selection, filling a gap in large-scale empirical evaluation.

Findings

Multiple-Output strategies outperform others

Deseasonalization improves forecast accuracy

Input selection is more effective with deseasonalization

Abstract

Multi-step ahead forecasting is still an open challenge in time series forecasting. Several approaches that deal with this complex problem have been proposed in the literature but an extensive comparison on a large number of tasks is still missing. This paper aims to fill this gap by reviewing existing strategies for multi-step ahead forecasting and comparing them in theoretical and practical terms. To attain such an objective, we performed a large scale comparison of these different strategies using a large experimental benchmark (namely the 111 series from the NN5 forecasting competition). In addition, we considered the effects of deseasonalization, input variable selection, and forecast combination on these strategies and on multi-step ahead forecasting at large. The following three findings appear to be consistently supported by the experimental results: Multiple-Output strategies are the best performing approaches, deseasonalization leads to uniformly improved forecast accuracy, and input selection is more effective when performed in conjunction with deseasonalization.