Comparison of Strategies for Multi-step-Ahead Prediction of Time Series Using Neural Network

If the one-step forecasting of a time series is already a challenging task, performing multi-step ahead forecasting is more difficult. Several approaches that deal with this complex problem have been proposed in literature: recursive (or iterated) strategy, direct strategy, combination of both the recursive and direct strategies, called DirREC, the Multi-Input Multi-Output (MIMO) strategy, and the last strategy, called DirMO which aims to preserve the advantageous aspects of both the Direct and MIMO strategies. This paper aims to review existing strategies for multi-step ahead forecasting using neural networks and compare their performances empirically. To attain such an objective, we performed several experiments of these different strategies on three datasets: NN3 competition dataset, the Vietnam composite stock price index (VNINDEX) and the closing prices of the FPT stock. The most consistent findings are that the DirREC strategy is better than all the other strategies for multi-step ahead forecasting using neural network.

[1]  Xiru Zhang,et al.  Time series analysis and prediction by neural networks , 1994 .

[2]  Amaury Lendasse,et al.  Methodology for long-term prediction of time series , 2007, Neurocomputing.

[3]  Antti Sorjamaa,et al.  Multiple-output modeling for multi-step-ahead time series forecasting , 2010, Neurocomputing.

[4]  Yen-Ming Chiang,et al.  Multi-step-ahead neural networks for flood forecasting , 2007 .

[5]  Marcus O'Connor,et al.  Artificial neural network models for forecasting and decision making , 1994 .

[6]  Rob J Hyndman,et al.  25 years of time series forecasting , 2006 .

[7]  M. Kubát.  An Introduction to Machine Learning , 2017, Springer International Publishing.

[8]  Guoqiang Peter Zhang,et al.  Neural network forecasting for seasonal and trend time series , 2005, Eur. J. Oper. Res..

[9]  Gianluca Bontempi,et al.  Long Term Time Series Prediction with Multi-Input Multi-Output Local Learning , 2008 .

[10]  Douglas Kline,et al.  Methods for Multi-Step Time Series Forecasting Neural Networks , 2004 .

[11]  Amaury Lendasse,et al.  Long-term prediction of time series by combining direct and MIMO strategies , 2009, 2009 International Joint Conference on Neural Networks.

[12]  Amaury Lendasse,et al.  Time series prediction using DirRec strategy , 2006, ESANN.

[13]  Diyar Akay,et al.  Comparison of direct and iterative artificial neural network forecast approaches in multi-periodic time series forecasting , 2009, Expert Syst. Appl..

[14]  Amir F. Atiya,et al.  A review and comparison of strategies for multi-step ahead time series forecasting based on the NN5 forecasting competition , 2011, Expert Syst. Appl..

[15]  G. C. Tiao,et al.  Some advances in non‐linear and adaptive modelling in time‐series , 1994 .

[16]  Michael Y. Hu,et al.  Forecasting with artificial neural networks: The state of the art , 1997 .

[17]  Gianluca Bontempi,et al.  Conditionally dependent strategies for multiple-step-ahead prediction in local learning , 2011 .

[18]  Thomas Kolarik,et al.  Time series forecasting using neural networks , 1994, APL '94.