Paper Info
Title
Pattern-Similarity-Based Model for Time Series Prediction
Abstract
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 ï =17 and ï =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.
Year
DOI
Venue
2015
10.1111/coin.12015
Computational Intelligence
Keywords
Field
DocType
artificial neural networks,time series prediction,hidden markov models
Time series,Fuzzy clustering,Pattern recognition,Computer science,Metric (mathematics),Lorenz system,Artificial intelligence,Soft computing,Artificial neural network,Cluster analysis,Hidden Markov model,Machine learning
Journal
Volume
Issue
ISSN
31
1
0824-7935
Citations 
PageRank 
References 
2
0.36
21
Authors
3
Name
Order
Citations
PageRank
Saurabh Bhardwaj1314.92
Smriti Srivastava213719.60
J. R. P. Gupta3516.26