Skip to main content

Improving Time Series Forecasting by Discovering Frequent Episodes in Sequences

  • Conference paper
Advances in Intelligent Data Analysis VIII (IDA 2009)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 5772))

Included in the following conference series:

Abstract

This work aims to improve an existing time series forecasting algorithm –LBF– by the application of frequent episodes techniques as a complementary step to the model. When real-world time series are forecasted, there exist many samples whose values may be specially unexpected. By the combination of frequent episodes and the LBF algorithm, the new procedure does not make better predictions over these outliers but, on the contrary, it is able to predict the apparition of such atypical samples with a great accuracy. In short, this work shows how to detect the occurrence of anomalous samples in time series improving, thus, the general forecasting scheme. Moreover, this hybrid approach has been successfully tested on electricity-related time series.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Basu, S., Meckesheimer, M.: Automatic outlier detection for time series: an application to sensor data. Knowledge and Information Systems 11(2), 137–154 (2007)

    Article  Google Scholar 

  2. Esparza, J., Heljanko, K.: Unfoldings: A Partial-Order Approach to Model Checking. Springer, Heidelberg (2008)

    MATH  Google Scholar 

  3. García-Martos, C., Rodríguez, J., Sánchez, M.J.: Mixed models for short-run forecasting of electricity prices: application for the Spanish market. IEEE Transactions on Power Systems 22(2), 544–552 (2007)

    Article  Google Scholar 

  4. Herui, C., Li, Y.: Short-term electricity price forecast based on improved fractal theory. In: Prooceedings of the eighth IEEE International Conference on Computer Engineering and Technology, pp. 347–351 (2009)

    Google Scholar 

  5. Jabłońska, M.: Analysis of outliers in electricity spot prices with example of New England and New Zealand markets. PhD thesis, Lappeenranta University, Finland (2008)

    Google Scholar 

  6. Li, G., Liu, C.C., Mattson, C., Lawarrée, J.: Day-ahead electricity price forecasting in a grid environment. IEEE Transactions on Power Systems 22(1), 266–274 (2007)

    Article  Google Scholar 

  7. Louni, H.: Outlier detection in ARMA models. Journal of Time Series Analysis 29(6), 1057–1065 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  8. Mannila, H., Toivonen, H., Verkamo, A.I.: Discovery of frequent episodes in event sequences. Data Mining and Knowledge Discovery 1, 259–289 (1997)

    Article  Google Scholar 

  9. Martínez-Álvarez, F., Troncoso, A., Riquelme, J.C., Ruiz, J.S.A.: LBF: A labeled-based forecasting algorithm and its application to electricity price time series. In: Prooceedings of the eighth IEEE International Conference on Data Mining, pp. 453–461 (2008)

    Google Scholar 

  10. Nanni, M., Rigotti, C.: Extracting trees of quantitative serial episodes. In: Džeroski, S., Struyf, J. (eds.) KDID 2006. LNCS, vol. 4747, pp. 170–188. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  11. Spanish Electricity Price Market Operator, http://www.omel.es

  12. Pino, R., Parreno, J., Gómez, A., Priore, P.: Forecasting next-day price of electricity in the Spanish energy market using artificial neural networks. Engineering Applications of Artificial Intelligence 21(1), 53–62 (2008)

    Article  Google Scholar 

  13. Troncoso, A., Riquelme, J.C., Riquelme, J.M., Martínez, J.L., Gómez, A.: Electricity market price forecasting based on weighted nearest neighbours techniques. IEEE Transactions on Power Systems 22(3), 1294–1301 (2007)

    Article  MATH  Google Scholar 

  14. Zhao, J.H., Dong, Z.Y., Li, X., Wong, K.P.: A framework for electricity price spike analysis with advanced data mining methods. IEEE Transactions on Power Systems 22(1), 376–385 (2007)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Martínez-Álvarez, F., Troncoso, A., Riquelme, J.C. (2009). Improving Time Series Forecasting by Discovering Frequent Episodes in Sequences. In: Adams, N.M., Robardet, C., Siebes, A., Boulicaut, JF. (eds) Advances in Intelligent Data Analysis VIII. IDA 2009. Lecture Notes in Computer Science, vol 5772. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03915-7_31

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-03915-7_31

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-03914-0

  • Online ISBN: 978-3-642-03915-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics