Paper Info
Title
Chaotic time series prediction using combination of Hidden Markov Model and Neural Nets
Abstract
This paper introduces a novel method for the prediction of chaotic time series using a combination of Hidden Markov Model (HMM) and Neural Network (NN). In this paper, an algorithm is proposed wherein an HMM, which is a doubly embedded stochastic process, is used for the shape based clustering of data. These data clusters are trained individually with Neural Network. The novel prediction approach used here exploits the Pattern Identification prowess of the HMM for cluster selection and uses the NN associated with each cluster to predict the output of the system. The effectiveness of the method is evaluated by using the benchmark chaotic time series: Mackey Glass Time Series (MGTS). Simulation results show that the given method provides a better prediction performance in comparison to previous methods.
Year
DOI
Venue
2010
10.1109/CISIM.2010.5643518
Computer Information Systems and Industrial Management Applications
Keywords
Field
DocType
chaos,hidden markov models,neural nets,pattern clustering,stochastic processes,time series,hmm,mackey glass time series,chaotic time series prediction,cluster selection,doubly embedded stochastic process,hidden markov model,pattern identification,prediction approach,shape based data clustering,neural networks,time series prediction,data clustering,neural net,stochastic process,neural network
Time series,Cluster (physics),Chaotic time series prediction,Pattern recognition,Computer science,Stochastic process,Artificial intelligence,Artificial neural network,Hidden Markov model,Cluster analysis,Chaotic,Machine learning
Conference
ISBN
Citations 
PageRank 
978-1-4244-7817-0
4
0.53
References 
Authors
11
4
Name
Order
Citations
PageRank
Saurabh Bhardwaj1314.92
Smriti Srivastava213719.60
Vaishnavi, S.340.53
J. R. P. Gupta440.53