Abstract
Automated forecasting is essential to business operations that handle scores of univariate time series. Practitioners have to deal with thousands of time series with a periodicity ranging from seconds to monthly. The sheer velocity and volume of time series make it challenging for human labour to manually identify the order of the time series to forecast the results. An automated forecasting algorithm or framework is essential to complete the task. The approach must be robust in the identification of the order of the time series, and readily applicable to scores of time series without manual intervention. The most modern automated forecasting algorithms are derived from exponential smoothing or ARIMA models. In this paper, the authors proposed a new heuristics approach to identify the initial starting point for a neighbourhood search to obtain the most appropriate model. The results of this method are used to compare against the methods proposed in the literature.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bergmeir, C., Hyndman, R.J., BenÃtez, J.M.: Bagging exponential smoothing methods using STL decomposition and Box-Cox transformation. Int. J. Forecast. 32(2), 303–312 (2016)
Box, G., Jenkins, G.: Time Series Analysis: Forecasting and Control. Holden-Day, San Francisco (1970)
Brockwell, P.J., Davis, R.A.: Time Series: Theory and Methods, 2nd edn. Springer, New York (1991)
Dickey, D.A., Fuller, W.A.: Likelihood ratio statistics for autoregressive time series with a unit root. Econom. J. Econom. Soc., 1057–1072 (1981)
Durbin, J., Koopman, S.J.: A simple and efficient simulation smoother for state space time series analysis. Biometrika 89(3), 603–616 (2002)
Keogh, E., Lin, J., Fu, A.: HOT SAX: efficiently finding the most unusual time series subsequence. In: The Fifth IEEE International Conference on Data Mining (2005)
Goldberger, A.L., Rigney, D.R.: Nonlinear dynamics at the bedside. In: Glass, L., Hunter, P., McCulloch, A. (eds.) Theory of Heart: Biomechanics, Biophysics, and Nonlinear Dynamics of Cardiac Function, pp. 583–605. Springer, New York (1991)
Gomez, V.: Automatic model identification in the presence of missing observations and outliers, Technical report, Ministerio de EconomÃa y Hacienda, Dirección General de Análisis y Programación Presupuestaria, working paper D-98009 (1998)
Gomez, V., Maravall, A.: Programs TRAMO and SEATS, instructions for the users, Technical report, Dirección General de Análisis y Programación Presupuestaria, Ministerio de EconomÃa y Hacienda, working paper 97001 (1998)
Goodrich, R.L.: The Forecast Pro Methodology, pp. 533–535 (2000)
Hannan, E.J., Rissanen, J.: Recursive estimation of mixed autoregressive- moving average order. Biometrika 69(1), 81–94 (1982)
Hendry, D.F., Doornik, J.A.: The implications for econometric modelling of forecast failure. Scott. J. Polit. Econ. 44(4), 437–461 (1997)
Hylleberg, S., et al.: Seasonal integration and cointegration. J. Econom. 44(1), 215–238 (1990)
Hyndman, R.J., Khandakar, Y.: Automatic time series for forecasting: the forecast package for R. No. 6/07. Monash University, Department of Econometrics and Business Statistics (2007)
Hyndman, R.J.: Data from the M-competitions. Comprehensive R Archive Network. http://cran.r-project.org/web/packages/Mcomp/
Kwiatkowski, D., et al.: 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)
Liu, L.M.: Identification of seasonal ARIMA models using a filtering method. Commun. Stat. A Theor. Methods 18, 2279–2288 (1989)
Makridakis, S., Hibon, M.: The M3-competition: results, conclusions and implications. Int. J. Forecast. 16, 451–476 (2000)
McCleary, R., Hay, R.: Applied Time Series Analysis for the Social Sciences. Sage Publications, Beverly Hills (1980)
Melard, G., Pasteels, J.-M.: Automatic ARIMA modeling including intervention, using time series expert software. Int. J. Forecast. 16, 497–508 (2000)
Ord, K., Lowe, S.: Automatic forecasting. Am. Stat. 50(1), 88–94 (1996)
Smith, J., Yadav, S.: Forecasting costs incurred from unit differencing fractionally integrated processes. Int. J. Forecast. 10(4), 507–514 (1994)
R Development Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, http://www.R-project.org (2008)
Reilly, D.: The AUTOBOX system. Int. J. Forecast. 16(4), 531–533 (2000)
Acknowledgement
We wish to thank Michele Hibon and Makridakis Spyros for providing M3 data; Hyndman [14, 15] for his R package, Forecast which provides us with the Auto.Arima function, and Mcomp, which allowed us to implement M3 time series data easily in R; and the anonymous referees for insightful comments and suggestions.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Choy, M., Laik, M.N. (2019). Intelligent Time Series Forecasting Through Neighbourhood Search Heuristics. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Advances in Information and Communication Networks. FICC 2018. Advances in Intelligent Systems and Computing, vol 887. Springer, Cham. https://doi.org/10.1007/978-3-030-03405-4_30
Download citation
DOI: https://doi.org/10.1007/978-3-030-03405-4_30
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-03404-7
Online ISBN: 978-3-030-03405-4
eBook Packages: EngineeringEngineering (R0)