Forecasting seasonal time series using fuzzy methods based on the SARIMA model
Date of Publication
2014
Document Type
Bachelor's Thesis
Degree Name
Bachelor of Science in Mathematics
Subject Categories
Physical Sciences and Mathematics
College
College of Science
Department/Unit
Mathematics and Statistics
Thesis Adviser
Paolo Lorenzo Y. Bautista
Defense Panel Chair
Frumencio F. Co
Defense Panel Member
Robert Neil F. Leong
Christopher Thomas R. Cruz
Abstract/Summary
Fuzzy time series is a useful alternative to conventional time series methods especially when there is uncertainty in the data. Further developments in the method have been created ever since its introduction in 1993. Although fuzzy time series is slowly getting recognized and more accepted as an alternative to crisp time series, few studies focus on data that have seasonality in them. Seasonal time series is present in stock markets, meteorology, agriculture, and more areas concerned with economics and nature, thus being frequently encountered in practice. There have been different methods of fuzzy time series in forecasting with seasonality. This paper focuses on developing a model guided by a seasonal ARIMA model, clustering the observations through fuzzy c-means, and determining fuzzy relationship using artificial neural networks. The method is compared with the performance of the SARIMA model.
Abstract Format
html
Language
English
Format
Electronic
Accession Number
CDTU019198
Shelf Location
Archives, The Learning Commons, 12F, Henry Sy Sr. Hall
Recommended Citation
Escarda, C. A., & Mateo, L. O. (2014). Forecasting seasonal time series using fuzzy methods based on the SARIMA model. Retrieved from https://animorepository.dlsu.edu.ph/etd_bachelors/18013
