Abstract
Most approaches to forecasting time series data employ one-step-ahead prediction approaches. However, recently there has been focus on multi-step-ahead prediction approaches. These approaches demonstrate enhanced prediction capabilities. However, multi-step-ahead prediction increases the complexity of the prediction process in comparison to one-step-ahead approaches. Typically, studies in the examination of multi-step ahead methods have addressed issues such as the increased complexity, inaccuracy, uncertainty, and error variance on the prediction horizon, and have been deployed in various domains such as finance, economics, agriculture and hydrology. When determining which algorithm to use in a time series analyses, the approach is to analyze the series for numerous characteristics and features, such as heteroscedasticity, auto-correlation, seasonality and stationarity. In this work, a comparative analysis of 20 different time series datasets is presented and a demonstration of the complexity in deciding which approach to use is given. The study investigates some of the main prediction approaches such as ARIMA (Autoregressive integrated moving average), NN (Neural Network), RNN (Recurrent neural network) and SVR (Support vector regression), which focus on the recursive prediction strategy and compare them to a new approach known as MRFA (Multi-Resolution Forecast Aggregation).
This work is supported by Science Foundation Ireland under grant number SFI/12/RC/2289.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Aweya, J.: Sensitivity methods for congestion control in computer networks. Ph.D thesis, Ottawa, Ontario, Canada, AAINQ48085 (1999)
Bahrpeyma, F., Roantree, M., McCarren, A.: Multi-resolution forecast aggregation for time series in agri datasets. In: Proceedings of the 25th Irish Conference on Artificial Intelligence and Cognitive Science, Dublin, Ireland, 7–8 December 2017, pp. 193–205 (2017)
Bontempi, G., Ben Taieb, S., Le Borgne, Y.-A.: Machine learning strategies for time series forecasting. In: Aufaure, M.-A., Zimányi, E. (eds.) eBISS 2012. LNBIP, vol. 138, pp. 62–77. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36318-4_3
Box, G.E.P., Jenkins, G.M., Reinsel, G.C., Ljung, G.M.: Time Series Analysis: Forecasting and Control. Wiley, Hoboken (2015)
Brockwell, P.J., Davis, R.A.: Introduction to Time Series and Forecasting. Springer, Heidelberg (2016). https://doi.org/10.1007/978-3-319-29854-2
Browne, A.: Neural Network Analysis, Architectures and Applications. CRC Press, Boca Raton (1997)
Cadavid, A.C., Lawrence, J.K., Ruzmaikin, A.: Principal components and independent component analysis of solar and space data. In: Ireland, J., Young, C.A. (eds.) Solar Image Analysis and Visualization. Springer, New York (2007). https://doi.org/10.1007/978-0-387-98154-3_5
Chatfield, C.: The Analysis of Time Series: An Introduction. CRC Press, New York (2016)
Chu, H., Wei, J., Li, T., Jia, K.: Application of support vector regression for mid-and long-term runoff forecasting in “yellow river headwater” region. Procedia Eng. 154, 1251–1257 (2016)
Corder, G.W., Foreman, D.I.: Nonparametric Statistics: A Step-by-Step Approach. Wiley, Hoboken (2014)
Dickey, D.A., Fuller, W.A.: Distribution of the estimators for autoregressive time series with a unit root. J. Am. Stat. Assoc. 74(366a), 427–431 (1979)
Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. Eur. J. Oper. Res. 270, 654–669 (2017)
Hurst, H.E.: Long term storage capacity of reservoirs. ASCE Trans. 116(776), 770–808 (1951)
Kantelhardt, J.W., Koscielny-Bunde, E., Rego, H.H.A., Havlin, S., Bunde, A.: Detecting long-range correlations with detrended fluctuation analysis. Phys. A Stat. Mech. Appl. 295(3–4), 441–454 (2001)
Kočenda, E., Černỳ, A.: Elements of Time Series Econometrics: An Applied Approach. Charles University in Prague, Karolinum Press, Prague (2015)
Kwiatkowski, D., Phillips, P.C.B., Schmidt, P., Shin, Y.: Testing the null hypothesis of stationarity against the alternative of a unit root: how sure are we that economic time series have a unit root? J. Econom. 54(1–3), 159–178 (1992)
Mandic, D.P., Chambers, J.A.: Recurrent Neural Networks for Prediction: Learning Algorithms, Architectures and Stability. Wiley Online Library (2001)
Parlos, A.G., Rais, O.T., Atiya, A.F.: Multi-step-ahead prediction using dynamic recurrent neural networks. Neural Netw. 13(7), 765–786 (2000)
Richman, J.S., Lake, D.E., Moorman, J.R.: Sample entropy. In: Methods in Enzymology, vol. 384, pp. 172–184. Elsevier (2004)
Soofi, A.S., Cao, L.: Modelling and Forecasting Financial Data: Techniques of Nonlinear Dynamics, vol. 2. Springer, New York (2012). https://doi.org/10.1007/978-1-4615-0931-8
Xiong, W., Xu, B.: Study on optimization of SVR parameters selection based on PSO. J. Syst. Simul. 9, 017 (2006)
Zhang, G., Hu, M.Y.: Neural network forecasting of the British pound/US dollar exchange rate. Omega 26(4), 495–506 (1998)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Bahrpeyma, F., Roantree, M., McCarren, A. (2018). Multistep-ahead Prediction: A Comparison of Analytical and Algorithmic Approaches. In: Ordonez, C., Bellatreche, L. (eds) Big Data Analytics and Knowledge Discovery. DaWaK 2018. Lecture Notes in Computer Science(), vol 11031. Springer, Cham. https://doi.org/10.1007/978-3-319-98539-8_26
Download citation
DOI: https://doi.org/10.1007/978-3-319-98539-8_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-98538-1
Online ISBN: 978-3-319-98539-8
eBook Packages: Computer ScienceComputer Science (R0)