Forecasting seasonal time series using fuzzy methods based on the SARIMA model
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 al...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | text |
| Language: | English |
| Published: |
Animo Repository
2014
|
| Subjects: | |
| Online Access: | https://animorepository.dlsu.edu.ph/etd_bachelors/18013 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | De La Salle University |
| Language: | English |
| 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. |
|---|