A Hybrid Prediction Algorithm for Traffic Speed Prediction

  • Bo-Wei Huang
  • Kun-Wei Wang
  • Ling-Yin Wei
  • Wen-Chih Peng
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 20)


Many types of data can be regarded as time series data. Therefore time series data predictions are applied in a wide range of domains, such as investment, traffic prediction, etc. Traffic status prediction can be used for congestion avoidance and travel planning. We solve the problem of predicting traffic status by time series prediction. The time series data prediction problem is that given a query time and time series data, we intend to predict the data value at the query time. Usually, a query time will be a future time. In this paper, we propose a hybrid prediction algorithm which exploits regression-based and clustering-based prediction methods. Explicitly, regression-based prediction is accurate when the query time is not too far from the current time. Note that time series data may have some similar shapes or trends. To capture the similar shapes hidden in this data, we utilize clustering concepts. Using these clusters, we could further discover their sequential relationships. As such, if the query time is far away from the current time, we utilize the above cluster sequential relationships to predict the possible similar cluster. From the similar cluster, the data value at the query time is obtained. Note that the hybrid algorithm aggregates the above two methods using one threshold that decides which method to use. If the time difference between the query time and the current time is smaller than the prediction length threshold, hybrid prediction uses regression-based prediction. Otherwise, our hybrid algorithm uses clustering-based prediction. To prove our proposed methods, we have carried out a set of experiments on real data sets to compare the accuracy of the methods. The results of the experiments prove that our proposed methods are both accurate and practical.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Bemdt, D.J., Clifford, J.: Using dynamic time warping to find patterns in time series. In: KDD Workshop, pp. 229–248 (1994)Google Scholar
  2. 2.
    Chatfield, C.: Time-Series Forecasting. Chapman and Hall/CRC (2001)Google Scholar
  3. 3.
    Chen, L., Ng, R.T.: On the marriage of lp-norms and edit distance. In: VLDB (2004)Google Scholar
  4. 4.
    Chen, L., Ozsu, M.T., Oria, V.: Robust and fast similarity search for moving object trajectories. In: SIGMOD, pp. 491–502 (2005)Google Scholar
  5. 5.
    Ester, M., Kriegel, H.-P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: KDD (1996)Google Scholar
  6. 6.
    Giles, C.L., Lawrence, S., Tsoi, A.C.: Noisy time series prediction using recurrent neural networks and grammatical inference. Machine Learning (2001)Google Scholar
  7. 7.
    Lin, J., Keogh, E., Wei, L., Lonardi, S.: Experiencing sax: a novel symbolic representation of time series. Data Mining and Knowledge Discovery 15(2), 107–144 (2007)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Lloyd, S.: Least squares quantization in pcm. IEEE Transactions on Information Theory (1982)Google Scholar
  9. 9.
    Ha, Y.-M., Park, S., Kim, S.-W., Won, J.-I., Yoon, J.-H.: Rule discovery and matching in stock databases. In: IEEE International Computer Software and Applications Conference (2008)Google Scholar
  10. 10.
    Morchen, F., Ultsch, A., Hoos, O.: Extracting interpretable muscle activation patterns with time series knowledge mining. International Journal of Knowledge Based Intelligent Engineering Systems 9(3), 197 (2005)Google Scholar
  11. 11.
    Ron, D., Singer, Y., Tishby, N.: The power of amnesia: Learning probabilistic automata with variable memory length. Machine Leraning (1996)Google Scholar
  12. 12.
    Ruta, D., Gabrys, B., Lemke, C.: A generic multilevel architecture for time series prediction. TKDE (2010)Google Scholar
  13. 13.
    Sant’Anna, A., Wickstrom, N.: Symbolization of time-series: An evaluation of sax, persist, and aca. In: 2011 4th International Congress on Image and Signal Processing (CISP), vol. 4, pp. 2223–2228. IEEE (2011)Google Scholar
  14. 14.
    Tsay, R.S.: Analysis of Financial Time Series. John Wiley&Sons (2002)Google Scholar
  15. 15.
    Vlachos, M., Kollios, G., Gunopulos, D.: Discovering similar multidimensional trajectories. In: ICDE, pp. 673–684 (2002)Google Scholar
  16. 16.
    Vlachos, M., Yu, P., Castelli, V.: On periodicity detection and structural periodic similarity. In: SDM (2005)Google Scholar
  17. 17.
    Wang, P., Wang, H., Wang, W.: Finding semantics in time series. In: SIGMOD (2011)Google Scholar
  18. 18.
    Xing, Z., Pei, J., Yu, P.S.: Early classification on time series. In: KAIS (2012)Google Scholar
  19. 19.
    Xiong, P., Chi, Y., Zhu, S., Moon, H.J., Pu, C., Hacıgümüs, H.: Intelligent management of virtualized resources for database systems in cloud environment. In: SDM (2005)Google Scholar
  20. 20.
    Yuan, H., Liu, J., Pu, H., Mao, J., Gao, S.: Prediction of chaotic ferroresonance time series based on the dynamic fuzzy neural network. In: ECAC (2012)Google Scholar
  21. 21.
    Zhou, F., Torre, F., Hodgins, J.K.: Aligned cluster analysis for temporal segmentation of human motion. In: 8th IEEE International Conference on Automatic Face & Gesture Recognition, FG 2008, pp. 1–7. IEEE (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Bo-Wei Huang
    • 1
  • Kun-Wei Wang
    • 1
  • Ling-Yin Wei
    • 1
  • Wen-Chih Peng
    • 1
  1. 1.National Chao-Tung UniversityHsinchuTaiwan, R.O.C.

Personalised recommendations