TY - JOUR
T1 - A hybrid approach based on winter's model and weighted fuzzy time series for forecasting trend and seasonal data
AU - Suhartono,
AU - Lee, Muhammad Hisyam
PY - 2011
Y1 - 2011
N2 - In the literature, the most studied of fuzzy time series for the purpose of forecasting is the first order fuzzy time series model. In this model, only the first lagged variable is used when constructing the first order fuzzy time series model. Therefore, such approaches fail to analyze accurately trend and seasonal time series which is an important class in time series models. Approach: In this paper, a hybrid approach is proposed in order to analyze trend and seasonal fuzzy time series. The proposed hybrid approach is based on Winter's model and weighted fuzzy time series. The Winter's model and the WFTS model are used jointly, aiming to capture different forms of pattern in the time series data. The order of this model is determined by utilizing graphical order fuzzy relationship. A real time series about tourist arrivals data is analyzed with this method to show the efficiency of the proposed hybrid method. Results: The results obtained from the proposed method are compared with the other methods, i.e., Decomposition, Winter's and ARIMA models. As a result, it is observed that more accurate results are obtained from the proposed hybrid method. Conclusion: The empirical results with tourist arrivals data clearly suggest that the hybrid model is able to outperform each component model used in isolation the pattern of time series data. Moreover, these empirical evidences suggest that by using dissimilar models or models that disagree each other strongly, the hybrid model will have lower generalization variance or error. Additionally, because of the possible unstable or changing patterns in the data, using the hybrid method can reduce the model uncertainty which typically occurred in statistical inference and time series forecasting.
AB - In the literature, the most studied of fuzzy time series for the purpose of forecasting is the first order fuzzy time series model. In this model, only the first lagged variable is used when constructing the first order fuzzy time series model. Therefore, such approaches fail to analyze accurately trend and seasonal time series which is an important class in time series models. Approach: In this paper, a hybrid approach is proposed in order to analyze trend and seasonal fuzzy time series. The proposed hybrid approach is based on Winter's model and weighted fuzzy time series. The Winter's model and the WFTS model are used jointly, aiming to capture different forms of pattern in the time series data. The order of this model is determined by utilizing graphical order fuzzy relationship. A real time series about tourist arrivals data is analyzed with this method to show the efficiency of the proposed hybrid method. Results: The results obtained from the proposed method are compared with the other methods, i.e., Decomposition, Winter's and ARIMA models. As a result, it is observed that more accurate results are obtained from the proposed hybrid method. Conclusion: The empirical results with tourist arrivals data clearly suggest that the hybrid model is able to outperform each component model used in isolation the pattern of time series data. Moreover, these empirical evidences suggest that by using dissimilar models or models that disagree each other strongly, the hybrid model will have lower generalization variance or error. Additionally, because of the possible unstable or changing patterns in the data, using the hybrid method can reduce the model uncertainty which typically occurred in statistical inference and time series forecasting.
KW - Fuzzy time series
KW - Graphical order
KW - Hybrid
KW - Seasonality
KW - Trend
KW - Winter's model
UR - http://www.scopus.com/inward/record.url?scp=84863116564&partnerID=8YFLogxK
U2 - 10.3844/jmssp.2011.177.183
DO - 10.3844/jmssp.2011.177.183
M3 - Article
AN - SCOPUS:84863116564
SN - 1549-3644
VL - 7
SP - 177
EP - 183
JO - Journal of Mathematics and Statistics
JF - Journal of Mathematics and Statistics
IS - 3
ER -