Paper Info
Title
A Fuzzy Time Series Model to Forecast the BDI
Abstract
Fuzzy time series models has been applied to forecast various problems and have been shown to forecast better than other models. In this article, we intend to apply Chou and Lee's fuzzy time series model to forecast the Baltic Dry Index (BDI) index for the next month. The root mean square error is one criteria to evaluate the forecasting performance. Empirical results show that the fuzzy time series model is suitable for the BDIpsilas prediction.
Year
DOI
Venue
2008
10.1109/NCM.2008.94
NCM (2)
Keywords
Field
DocType
economic forecasting,baltic dry index,fuzzy forecasting,fuzzy set theory,next month,economic indicators,transportation,fuzzy time series,forecasting performance,fuzzy time series model,square error,empirical result,various problem,bdi,time series,mean square error methods,indexation,fuzzy sets,root mean square error,time series analysis,forecasting,mathematical model,indexes,time series model,predictive models
Time series,Forecast skill,Data mining,Economic forecasting,Computer science,Fuzzy logic,Economic indicator,Mean squared error,Fuzzy set,Artificial intelligence,Forecast verification,Machine learning
Conference
Volume
ISBN
Citations 
2
978-0-7695-3322-3
3
PageRank 
References 
Authors
0.53
5
1
Name
Order
Citations
PageRank
Ming-Tao Chou1767.15