A Comparative Analysis of Short Time Series Processing Methods

Arnis Kirshners, Arkady Borisov


This article analyzes the traditional time series processing methods that are used to perform the task of short time series analysis in demand forecasting. The main aim of this paper is to scrutinize the ability of these methods to be used when analyzing short time series. The analyzed methods include exponential smoothing, exponential smoothing with the development trend and moving average method. The paper gives the description of the structure and main operating principles. The experimental studies are conducted using real demand data. The obtained results are analyzed; and the recommendations are given about the use of these methods for short time series analysis.


Forecasting; exponential smoothing; exponential smoothing with the development trend; moving average method; short time series

Full Text:



J. J. Flores and R. Loaeza, Financial time series forecasting using a hybrid neural-evolutive approach, Proceedings of the XV SIGEF International Conference, Lugo, Spain, 2009, pp. 547-555.

E. de Alba and M. Mendoza, Bayesian Forecasting Methods for Short Time Series, Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, Issue 8, 2007, pp. 41-44.

J. S. Armstrong, F. Collopy and J. T. Yokum, Decomposition by causal forces: A procedure for forecasting complex time series, International Journal of Forecasting, 21, 2005, pp. 25-36.

D. C. Montgomery, C. L. Jennings and M. Kulachi, Introduction to time series analysis and forecasting. Wiley-interscience, 2008.

J. Ernst, G. J. Nau and Z. Bar-Joseph, Clustering short time series gene expression data, Bioinformatics, Vol. 21, No. suppl_1, 2005, pp. 159-168.

I. H. Written and E. Frank, Data mining: practical machine learning tools and techniques - 2nd edition. Amsterdam etc.: Morgan Kaufman, 2005.

Jr. E.S. Gardner, Exponential Smoothing: The State of Art, Journal of Forecasting 4, 1985, pp. 1-28.

K. K. Boyer and R. Verma, Operations and Supply Chain Management for the 21st Century, USA: South-Western Cengage Learning, 2010.


  • There are currently no refbacks.

Copyright (c) 2012 Arnis Kirshners, Arkady Borisov

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.