Browse > Article
http://dx.doi.org/10.5351/KJAS.2022.35.3.385

Performance for simple combinations of univariate forecasting models  

Lee, Seonhong (Department of Applied Statistics, Chung-Ang University)
Seong, Byeongchan (Department of Applied Statistics, Chung-Ang University)
Publication Information
The Korean Journal of Applied Statistics / v.35, no.3, 2022 , pp. 385-393 More about this Journal
Abstract
In this paper, we consider univariate time series models that are well known in the field of forecasting and we study on forecasting performance for their simple combinations. The univariate time series models include exponential smoothing methods and ARIMA (autoregressive integrated moving average) models, their extended models, and non-seasonal and seasonal random walk models, which is frequently used as benchmark models for forecasting. The median and mean are simply used for the combination method, and the data set used for performance evaluation is M3-competition data composed of 3,003 various time series data. As results of evaluating the performance by sMAPE (symmetric mean absolute percentage error) and MASE (mean absolute scaled error), we assure that the simple combinations of the univariate models perform very well in the M3-competition dataset.
Keywords
univariate time series models; exponential smoothing methods; ARIMA; M3-competition;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Livera AMD, Hyndman RJ, and Snyder RD (2011). Forecasting time series with complex seasonal patterns using exponential smoothing, Journal of the American Statistical Association, 106(496), 1513-1527.   DOI
2 Holt CE (1957). Forecasting seasonals and trends by exponentially weighted averages, O.N.R. Memorandum No. 52. Carnegie Institute of Technology, Pittsburgh USA.
3 Kolassa S (2011). Combining exponential smoothing forecasts using Akaike weights, International Journal of Forecasting, 27, 238-251.   DOI
4 Assimakopoulos V and Nikolopoulos K (2000). The Theta model: a decomposition approach to forecasting, International Journal of Forecasting, 16(4), 521-530.   DOI
5 Brown RG (1959). Statistical forecasting for inventory control, McGraw/Hill.
6 Commandeur JJF and Koopman SJ (2007). Introduction to State Space Time Series Analysis, Oxford University Press.
7 Makridakis S and Hibon M (2000). The M3-Competition: results, conclusions and implications, International Journal of Forecasting, 16(4), 451-476.   DOI
8 Hyndman RJ and Athanasopoulos G (2018). Forecasting: principles and practice, 2nd Ed., OTexts.
9 Lichtendahl KC, Grushka-Cockayne Y, and Winkler RL (2013). Is it better to average probabilities or quantiles? Management Science, 59(7), 1594-1611.   DOI
10 Makridakis S (1993). Accuracy measures: theoretical and practical concerns, International Journal of Forecasting, 9(4), 527-529.   DOI
11 Petropoulos F and Svetunkov I (2020). A simple combination of univariate models, International Journal of Forecasting, 36(1), 110-115.   DOI
12 Hyndman RJ and Koehler AB (2006). Another look at measures of forecast accuracy, International Journal of Forecasting, 22(4), 679-688.   DOI
13 Fiorucci JA, Pellegrini TR, Louzada F, Petropoulos F, and Koehler AB (2016). Models for optimising the theta method and their relationship to state space models. International Journal of Forecasting, 32(4), 1151-1161.   DOI
14 Box GEP and Jenkins GM (1970). Time series analysis: forecasting and control, Holden-Day, San Francisco.
15 Svetunkov I and Kourentzes N (2018). Complex exponential smoothing for seasonal time series, Working Paper of Department of Management Science, Lancaster University, 2018:1, 1-20.