Browsing by Author "Tseng, FM"

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A fuzzy seasonal ARIMA model for forecasting
(2002-03-16)
Tseng, FM
;
Tzeng, GH
;
科技管理研究所
;
Institute of Management of Technology
This paper proposes a fuzzy seasonal ARIMA (FSARIMA) forecasting model, which combines the advantages of the seasonal time series ARIMA (SARIMA) model and the fuzzy regression model. It is used to forecast two seasonal time series data of the total production value of the Taiwan machinery industry and the soft drink time series. The intention of this paper is,to provide business which are affected by diversified management with a new method to conduct short-term forecasting. This model includes both interval models with interval parameters and the possible distribution of future value. Based on the results of practical application, it can be shown that this model makes good forecasts and is realistic. Furthermore, this model makes it possible for decision makers to forecast the best and worst estimates based on fewer observations than the SARIMA model. (C) 2002 Elsevier Science B.V. All rights reserved.
Applied hybrid grey model to forecast - Seasonal time series
(2001-06-01)
Tseng, FM
;
Yu, HC
;
Tzeng, GH
;
科技管理研究所
;
Institute of Management of Technology
The grey forecasting model has been successfully applied to finance, physical control, engineering, economics, etc. However, no seasonal time series forecast has been tested. The authors of this paper proved that GM(1,1) grey forecasting model is insufficient for forecasting time series with seasonality. This paper proposes a hybrid method that combines the GM(1,1) grey forecasting model and the ratio-to-moving-average deseasonalization method to forecast time series with seasonality characteristics. Three criteria, i.e., the mean squares error (MSE), the mean absolute error (MAE), and mean absolute percentage error (MAPE) were used to compare the performance of the hybrid model against other four models, i.e., the seasonal time series ARIMA model (SARIMA), the neural network back-propagation model combined with grey relation, the GM(1,1) grey model with raw data, the GM(1,N) grey model combined with grey relation. The time series data of the total production value of Taiwan's machinery industry (January 1994 to December 1997) and the sales volume of soft drink reported from Montgomery's book were used as test data sets. Except for the out-of-sample error of the Taiwan machinery production value time series, the MSE, the MAE, and the MAPE of the hybrid model were the lowest. (C) 2001 Elsevier Science Inc.
Combining neural network model with seasonal time series ARIMA model
(2002-01-01)
Tseng, FM
;
Yu, HC
;
Tzeng, GH
;
科技管理研究所
;
Institute of Management of Technology
This paper proposes a hybrid forecasting model, which combines the seasonal time series ARIMA (SARIMA) and the neural network back propagation (BP) models, known as SARIMABP. This model was used to forecast two seasonal time series data of total production value for Taiwan machinery industry and the soft drink time series. The forecasting performance was compared among four models, i.e., the SARIMABP and SARIMA models and the two neural network models with differenced and deseasonalized data, respectively. Among these methods, the mean square error (MSE), the mean absolute error (MAE), and the mean absolute percentage error (MAPE) of the SARIMABP model were the lowest. The SARIMABP model was also able to forecast certain significant turning points of the test time series. (C) 2002 Elsevier Science Inc. All rights reserved.
Fuzzy ARIMA model for forecasting the foreign exchange market
(2001-02-16)
Tseng, FM
;
Tzeng, GH
;
Yu, HC
;
Yuan, BJC
;
管理學院
Considering the time-series ARTMA (p,d,q) model and fuzzy regression model, this paper develops a fuzzy ARIMA (FARIMA) model and applies it to forecasting the exchange rate of NT dollars to US dollars. This model includes interval models with interval parameters and the possibility distribution of future values is provided by FARIMA. This model makes it possible for decision makers to forecast the best- and worst-possible situations based on fewer observations than the ARIMA model. (C) 2001 Elsevier Science B.V. All rights reserved.
Hierarchical fuzzy integral stated preference method for Taiwan's broadband service market
(2005-02-01)
Tseng, FM
;
Chiu, YJ
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科技管理研究所
;
Institute of Management of Technology
The stated preference method (or conjoint analysis) has been a popular method for measuring buyer tradeoffs among multi-attribute products or services. In addition, the multi-nomial logit (MNL) model is a popular model for the stated preference method, although it relies on the assumption that its attributes have no correlations. However, in social problems, attributes are often correlated with each other. Recently, the Choquet integral has been used to solve non-additive problems. This study combines the Choquet integral and the stated preference method to propose the hierarchical fuzzy integral stated preference (HFISP) method, and then analyze the narrowband service users' preferences for broadband service. The results demonstrate that the hierarchical fuzzy integral stated preference method performs better than the partitioned fuzzy integral multi-nomial logit (PFIMNL) model. The HFISP method is effective and can be applied to deal with real life problems since it solves correlated attribute problem in discrete choice behavior. (C) 2004 Elsevier Ltd. All rights reserved.