LOGIN TO YOUR ACCOUNT

Username
Password
Remember Me
Or use your Academic/Social account:

CREATE AN ACCOUNT

Or use your Academic/Social account:

Congratulations!

You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.

Important!

Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message

CREATE AN ACCOUNT

Name:
Username:
Password:
Verify Password:
E-mail:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Fioruci, J.A.; Pellegrini, T.R.; Louzada, F.; Petropoulos, Fotios; Koehler, A.B. (2016)
Publisher: Elsevier BV
Languages: English
Types: Article
Subjects: Business and International Management
Accurate and robust forecasting methods for univariate time series are very important when the objective is to produce estimates for large numbers of time series. In this context, the Theta method’s performance in the M3-Competition caught researchers’ attention. The Theta method, as implemented in the monthly subset of the M3-Competition, decomposes the seasonally adjusted data into two “theta lines”. The first theta line removes the curvature of the data in order to estimate the long-term trend component. The second theta line doubles the local curvatures of the series so as to approximate the short-term behaviour. We provide generalisations of the Theta method. The proposed Dynamic Optimised Theta Model is a state space model that selects the best short-term theta line optimally and revises the long-term theta line dynamically. The superior performance of this model is demonstrated through an empirical application. We relate special cases of this model to state space models for simple exponential smoothing with a drift.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • Assimakopoulos, V. (1995). A sucessive filtering technique for identifying long-term trends. Journal of Forecasting, 14, 35-43.
    • Assimakopoulos, V., & Nikolopoulos, K. (2000). The theta model: a decomposition approach to forecasting. International Journal of Forecasting, 16(4), 521-530.
    • Athanasopoulos, G., Hyndman, R. J., Song, H., & Wu, D. C. (2011). The tourism forecasting competition. International Journal of Forecasting, 27, 822-844.
    • Boylan, J., Goodwin, P., Mohammadipour, M., & Syntetos, A. (2015). Reproducibility in forecasting research. International Journal of Forecasting, 31, 79-90.
    • Brown, R. G. (1956). Exponential smoothing for predicting demand. Cambridge, Massachusetts: Arthur D. Little Inc.
    • Clemen, R. T. (1989). Combining forecasts: A review and annotated bibliography. International Journal of Forecasting, 5, 559-583.
    • Constantinidou, C., Nikolopoulos, K., Bougioukos, N., Tsiafa, E., Petropoulos, F., & Assimakopoulos, V. (2012). A neural network approach for the theta model. In Lecture notes in information technology: Vol. 25. Information engineering (pp. 116-120).
    • Franses, P. H., & Legerstee, R. (2011). Combining SKU-level sales forecasts from models and experts. Expert Systems with Applications, 38, 2365-2370.
    • Gardner, J. E. S., & McKenzie, E. (1985). Forecasting trends in time series. Management Science, 31, 1237-1246.
    • Goodwin, P., & Lawton, R. (1999). On the asymmetry of the symmetric mape. International Journal of Forecasting, 15, 405-408.
    • Hyndman, R. J., & Billah, B. (2003). Unmasking the theta method. International Journal of Forecasting, 19, 287-290.
    • Hyndman, R., & Khandakar, Y. (2008). Automatic time series forecasting: the forecast package for R. Journal of Statistical Software, 27, 1-22.
    • Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22, 679-688.
    • Hyndman, R. J., Koehler, A. B., Snyder, R. D., & Grose, S. (2002). A state space framework for automatic forecasting using exponential smoothing methods. International Journal of Forecasting, 18, 439-454.
    • Koning, A. J., Franses, P. H., Hibon, M., & Stekler, H. O. (2005). The M3 competition: Statistical tests of the results. International Journal of Forecasting, 21(3), 397-409.
    • Makridakis, S., & Hibon, M. (2000). The M3-competition: results, conclusions and implications. International Journal of Forecasting, 16, 451-476.
    • Makridakis, S., & Winkler, R. L. (1983). Averages of forecasts: Some empirical results. Management Science, 29, 987-996.
    • Nikolopoulos, K., & Assimakopoulos, V. (2005). Fathoming the theta model. In 25th International symposium on forecasting, ISF, San Antonio, Texas, USA. unknown.
    • Nikolopoulos, K., Assimakopoulos, V., Bougioukos, N., Litsa, A., & Petropoulos, F. (2011). The Theta model: An essential forecasting tool for supply chain planning. In G. Lee (Ed.), Lecture notes in electrical engineering: Vol. 123. Advances in automation and robotics, Vol. 2 (pp. 431-437). Berlin, Heidelberg: Spinger-Verlag.
    • Nikolopoulos, K., Thomakos, D., Petropoulos, F., Litsa, A., & Assimakopoulos, V. (2012). Forecasting S&P 500 with the theta model. International Journal of Financial Economics and Econometrics, 4, 73-78.
    • Petropoulos, F., Makridakis, S., Assimakopoulos, V., & Nikolopoulos, K. (2014). 'horses for courses' in demand forecasting. European Journal of Operational Research, 237(1), 152-163.
    • Petropoulos, F., & Nikolopoulos, K. (2013). Optimizing Theta model for monthly data. In Proceedings of the 5th International conference on agents and artificial intelligence.
    • Poler, R., & Mula, J. (2011). Forecasting model selection through outof-sample rolling horizon weighted errors. Expert Systems with Applications, 38, 14778-14785.
    • R Core Team (2015). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing, ISBN: 3-900051-07-0, URL: http://www.R-project.org/.
    • Thomakos, D., & Nikolopoulos, K. (2014). Fathoming the theta method for a unit root process. IMA Journal of Management Mathematics, 25, 105-124.
    • Thomakos, D., & Nikolopoulos, K. (2015). Forecasting multivariate time series with the theta method. Journal of Forecasting, 34, 220-229.
    • Tiago R. Pellegrini obtained his first degree in Statistics from Federal University of São Carlos (UFSCar) in 2010, M.Sc. in Statistics from UFSCar in 2012 and is a Ph.D. student in the Department of Mathematics and Statistics at University of New Brunswick. Current research interests are related to spatial models, time series analysis and computational statistics.
    • Francisco Louzada is a Full Professor in the Institute of Mathematical Science and Computing, University of São Paulo (USP), Brazil; director for the Center for Risk Analysis, USP/UFSCar, Brazil; CNPq Productivity Researcher level 1B. He received his Ph.D. degree in Statistics from the University of Oxford, UK, his M.Sc. degree in Computational Mathematics from USP, and his B.Sc. degree from UFSCar, Brazil. His main interests are survival analysis, data mining and statistical inference. More information: http://www.mwstat.com/franciscolouzada.
  • No related research data.
  • No similar publications.

Share - Bookmark

Cite this article