PATTERN-SIMILARITY-BASED MODEL FOR TIME SERIES PREDICTION
Article first published online: 6 AUG 2013
© 2013 Wiley Periodicals, Inc.
How to Cite
Bhardwaj, S., Srivastava, S. and Gupta, J. R. P. (2013), PATTERN-SIMILARITY-BASED MODEL FOR TIME SERIES PREDICTION. Computational Intelligence. doi: 10.1111/coin.12015
- Article first published online: 6 AUG 2013
- Manuscript Accepted: 13 JUN 2013
- Manuscript Revised: 20 MAY 2013
- Manuscript Received: 14 AUG 2012
- hidden Markov models;
- fuzzy inference systems;
- artificial neural networks;
- shape-based clustering;
- time series prediction
This research proposes a pattern/shape-similarity-based clustering approach for time series prediction. This article uses single hidden Markov model (HMM) for clustering and combines it with soft computing techniques (fuzzy inference system/artificial neural network) for the prediction of time series. Instead of using distance function as an index of similarity, here shape/pattern of the sequence is used as the similarity index for clustering, which overcomes few of the shortcomings associated with distance-based clustering approaches. Underlying hidden properties of time series are captured with the help of HMM. The prediction method used here exploits the pattern identification prowess of the HMM for cluster selection and the generalization and nonlinear modeling capabilities of soft computing methods to predict the output of the system. To see the validity of the proposed method in the real-life scenario, it is tested on four different time series. The first is a benchmark Mackey–Glass time series, which is tested for delay parameters τ = 17 and τ = 30. The remaining time series are monthly sunspot data time series, Laser data time series and the last is Lorenz attractor time series. Simulation results show that the proposed method provide a better prediction performance in comparison with the existing methods.