Paper Info
Title
A novel shape based batching and prediction approach for time series using HMMs and FISs
Abstract
This paper introduces a novel approach which uses a Hidden Markov Model (HMM) based Fuzzy Inference System (FIS) for prediction of systems that are non deterministic, dynamical and chaotic in nature. The HMM is used for shape based batch creation of training data which is then processed one batch at a time by a FIS. The Membership functions and Rule Base of the FIS are tweaked to predict the correct output for an input dataset. The novel Prediction method used here exploits the Pattern Identification prowess of the HMM for batch selection and the FIS of each batch to predict the output of the system. The Benchmark applications of the Mackey Glass Time Series (MGTS) as well as the Sunspot Data time-series were used for testing the competence of this method.
Year
DOI
Venue
2010
10.1109/ISDA.2010.5687070
ISDA
Keywords
Field
DocType
time series prediction,fuzzy set theory,fuzzy reasoning,shape based batching,mgts,fuzzy inference system,hmm,fis,sunspot data time-series,membership function,mackey glass time series,shape based batch processing,hidden markov models,pattern identification,rule base,prediction approach,shape based batch creation,fuzzy inference systems,time series,hidden markov model,sun,glass,rule based,time series analysis,shape,training data,batch process
Training set,Time series,Pattern recognition,Computer science,Fuzzy set,Artificial intelligence,Hidden Markov model,Chaotic,Membership function,Machine learning,Pattern identification,Fuzzy inference system
Conference
ISBN
Citations 
PageRank 
978-1-4244-8134-7
4
0.49
References 
Authors
11
4
Name
Order
Citations
PageRank
Smriti Srivastava113719.60
Saurabh Bhardwaj2314.92
Advait Madhvan340.49
J. R. P. Gupta4516.26