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• W1966295073 endingPage "3372" @default.
• W1966295073 startingPage "3366" @default.
• W1966295073 abstract "Neural networks have been popular due to their capabilities in handling nonlinear relationships. Hence, this study intends to apply neural networks to implement a new fuzzy time series model to improve forecasting. Differing from previous studies, this study includes the various degrees of membership in establishing fuzzy relationships, which assist in capturing the relationships more properly. These fuzzy relationships are then used to forecast the stock index in Taiwan. With more information, the forecasting is expected to improve, too. In addition, due to the greater amount of information covered, the proposed model can be used to forecast directly regardless of whether out-of-sample observations appear in the in-sample observations. This study performs out-of-sample forecasting and the results are compared with those of previous studies to demonstrate the performance of the proposed model." @default.
• W1966295073 created "2016-06-24" @default.
• W1966295073 creator A5001603846 @default.
• W1966295073 creator A5049428656 @default.
• W1966295073 date "2010-04-01" @default.
• W1966295073 modified "2023-10-16" @default.
• W1966295073 title "A neural network-based fuzzy time series model to improve forecasting" @default.
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• W1966295073 doi "https://doi.org/10.1016/j.eswa.2009.10.013" @default.
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