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A bivariate fuzzy time series model to forecast the TAIEX
TLDR
This study intends to apply neural networks to fuzzy time series forecasting and propose bivariate models in order to improve forecasting. Expand
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A neural network-based fuzzy time series model to improve forecasting
TLDR
Neural networks have been popular due to their capabilities in handling nonlinear relationships. Expand
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The application of neural networks to forecast fuzzy time series
TLDR
Fuzzy time series models have been applied to handle nonlinear problems. Expand
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A Type 2 fuzzy time series model for stock index forecasting
Most conventional fuzzy time series models (Type 1 models) utilize only one variable in forecasting. Furthermore, only part of the observations in relation to that variable are used. To utilize moreExpand
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A Multivariate Heuristic Model for Fuzzy Time-Series Forecasting
TLDR
Fuzzy time-series models have been widely applied due to their ability to handle nonlinear data directly and because no rigid assumptions for the data are needed. Expand
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Viral effects of social network and media on consumers’ purchase intention
This study applies structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA) method to data from social network media (SNM) users’ surveys to identify possible SNMExpand
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Factors affecting the success of women entrepreneurs
The main purpose of this paper is to examine the relation that exists between the skills possessed by women entrepreneurs and their motivations, barriers and performance. Thus, on the theoreticalExpand
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Global entrepreneurship and innovation in management
Abstract The special issue discusses the global entrepreneurship and innovation with original perspectives and advanced knowledge. Entrepreneurship plays a critical role in creating value and inExpand
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An Advanced Approach to Forecasting Tourism Demand in Taiwan
Abstract Forecasting has been considered important in a service industry. Many techniques have been applied to improve forecasting results. This study intends to apply a neural network based fuzzyExpand
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