Abstract
Time series forecasting have attracted a great deal of attention from various research communities. There are many methods which divide time series into subseries. Information granules, fuzzy clustering and data segmentation are among the most popular methods in this field. However all these methods are designed to recognize dependencies between adjacent points. In order to do so, they divide the time series into time intervals. This imply some limitations in findings strongly non-local dependencies between points scatter across whole time series. The Divide and Conquer ensemble algorithm here presented was designed to address such limitations. The model samples the series into many subseries, searches for possible patterns and finally chooses the most significant subseries for further investigation. Since the prediction error evaluated on the subseries is lower than the one evaluated on the original time-series, the proposed strategy can significantly mitigate the overall prediction error. In order to evaluate the efficiency of our approach we performed the analysis on various artificial datasets. In a real world example our algorithm showed a 3-fold improvement of the accuracy with respect to other state-of-the-art methods. Although the algorithm was designed for time-series forecasting, it can be easily used for noise filtering purposes. Simulations reported in the present work illustrate the potential of the method in this field of application.
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
Australian Bureau of Statistics. https://datamarket.com/data/set/22xn/quarterly-australian-gross-farm-product-m-198990-prices-sep-59-mar-93/, Accessed 19-July-2015
de Boor, C.: A practical guide to splines (1978)
Karypis, G., Han, E.H., Kumar, V.: Chameleon: hierarchical clustering using dynamic modeling. Computer 32, 68–75 (1999)
Wu, H., Sharp, G., Salzberg, B., Kaeli, D., Shirato, H., Jiang, S.: Subsequence matching on structured time series data. In: SIGMOD (2005)
Hppner, F.: Knowledge discovery from sequential data (2002)
Han, J., Kamber, M.: Data mining: Concepts and techniques. Morgan Kaufmann, San Francisco (2001)
Han, J., Kamber, M.: Application of neural networks to an emerging financial market: forecasting and trading the taiwan stock index. Comput. Oper. Res. 30, 901–923 (2003)
Lin, J., Keogh, E., Wei, L., Lonardi, S.: Experiencing sax: a novel symbolic representation of time series. Data Min. Knowl. Discov. 15(2), 107–144 (2007)
JF, A.: Maintaining knowledge about temporal intervals, pp. 832–843 (1983)
Keogh, E.: A survey and novel approach, pp. 1–22 (2004)
Kovai, Z.: Time series analysis, faculty of economics (1995)
La, Z.: Fuzzy sets and information granularity, pp. 3–18 (1979)
Ester, M., Kriegel, H.-P., Jiirg, S., Xiaowei, X.: A densitybased algorithm for discovering clusters in large spatial databases. In: Proceedings of the 1996 International Conference on Knowledge Discovery and Data Mining (KDD 1996) (1996)
MacQueen, J.: Some methods for classification and analysis of multivariate observations. In: Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability 1, pp. 281–297. University of California Press (1967)
Marquardt, D.: An algorithm for least-squares estimation of nonlinear parameters. SIAM J. Appl. Math. 11(2), 431–441 (1963)
Cheeseman, P., Stutz, J.: Sting: a statistical information grid approach to spatial data mining. Bayesian classification (AutoClass): theory and results. In: Fayyard, U.M., Piatetsky-Shapiro, G., Smyth, P., Uthurusamy, R. (eds.) Advances in Knowledge Discovery and Data Mining. AAAI/MIT Press, Cambridge, MA (1996)
Pedrycz, W., Vukovich, G.: Abstraction and specialization of information granules, pp. 106–111 (2001)
Ramsay, J.O., Silverman, B.W.: Functional data analysis (1997)
Makridakis, S., Wheelwright, S., Hyndman, R.: Forecasting: Methods and applications. Wiley, New York (1997)
Song, H.J., Shen, Z.Q., Miao, C., Miao, Y.C.: Fuzzy cognitive map learning based on multi-objective particle swarm optimization. IEEE Trans. Fuzzy 18(2), 233–250 (2010)
Tong, H.: Threshold models in non-linear time series analysis. Springer, Heidelberg (1983)
Wang, W., Yang, J., Reeves, M.R.: Sting: a statistical information grid approach to spatial data mining. In: Proceedings of the 1997 International Conference on Very Large Data Base (VLDB 1997) (1997)
Wang, W., WitoldPedry, X.L.: Time series long-term forecasting model based on information granules and fuzzy clustering, pp. 17–24 (2015)
Zhang, G.: Time series forecasting using a hybrid arima and neural network model. Neurocomputing 50, 159–175 (2003)
Zhang, G.: A neural network ensemble method with jittered training data for time series forecasting. Inf. Sci. 177, 5329–5346 (2007)
Acknowledgements
This research was supported by the European Union from financial resources of the European Social Fund, Project PO KL Information technologies: Research and their interdisciplinary applications and by the Polish National Science Centre with the grants 2014/15/B/ST6/05082 and 2013/09/B/NZ2/00121.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Kostrzewa, J., Mazzocco, G., Plewczynski, D. (2016). Divide and Conquer Ensemble Method for Time Series Forecasting. In: Nguyen, N., Kowalczyk, R., Filipe, J. (eds) Transactions on Computational Collective Intelligence XXIV. Lecture Notes in Computer Science(), vol 9770. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-53525-7_8
Download citation
DOI: https://doi.org/10.1007/978-3-662-53525-7_8
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-53524-0
Online ISBN: 978-3-662-53525-7
eBook Packages: Computer ScienceComputer Science (R0)