Paper Info
Title
Partitions based computational method for high-order fuzzy time series forecasting
Abstract
In this paper, we present a computational method of forecasting based on multiple partitioning and higher order fuzzy time series. The developed computational method provides a better approach to enhance the accuracy in forecasted values. The objective of the present study is to establish the fuzzy logical relations of different order for each forecast. Robustness of the proposed method is also examined in case of external perturbation that causes the fluctuations in time series data. The general suitability of the developed model has been tested by implementing it in forecasting of student enrollments at University of Alabama. Further it has also been implemented in the forecasting the market price of share of State Bank of India (SBI) at Bombay Stock Exchange (BSE), India. In order to show the superiority of the proposed model over few existing models, the results obtained have been compared in terms of mean square and average forecasting errors.
Year
DOI
Venue
2012
10.1016/j.eswa.2012.04.039
Expert Syst. Appl.
Keywords
Field
DocType
higher order,high-order fuzzy time series,fuzzy logical relation,developed model,different order,fuzzy time series,computational method,existing model,developed computational method,average forecasting error,time invariant,time variant
Logical relations,Data mining,Mean square,Time series,LTI system theory,Computer science,Market price,Fuzzy logic,Robustness (computer science),Stock exchange,Artificial intelligence,Machine learning
Journal
Volume
Issue
ISSN
39
15
0957-4174
Citations 
PageRank 
References 
24
0.74
18
Authors
2
Name
Order
Citations
PageRank
Sukhdev Singh Gangwar1281.81
Sanjay Kumar2392.49