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AI Summary & Key Takeaways
AI Insight⚡ TL;DR(Too Long; Didn't Read)
The historical data of daily average temperature in Taipei, Taiwan (central weather bureau 1996 ) are adopted to illustrate the forecasting process of the proposed method.
📌 3 Key Takeaways
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Objective & Hypothesis:Since the pioneering work of Song and Chissom (1993 a,b ) on fuzzy time series to model and forecast processes whose values are described by linguistic values, a number of techniques have been proposed by researchers for forecasting.
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Key Findings & Data:In most of the realistic situation the duplicates of data are significant.
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Conclusion & Impact:The historical data of daily average temperature in Taipei, Taiwan (central weather bureau 1996 ) are adopted to illustrate the forecasting process of the proposed method.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Multiset · Forecasting · Fuzzy time series
Abstract
Since the pioneering work of Song and Chissom (1993 a,b ) on fuzzy time series to model and forecast processes whose values are described by linguistic values, a number of techniques have been proposed by researchers for forecasting. In most of the realistic situation the duplicates of data are significant. This paper presents a new fuzzy time series method, which employs multiset theory. The historical data of daily average temperature in Taipei, Taiwan (central weather bureau 1996 ) are adopted to illustrate the forecasting process of the proposed method.
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Cite This Article
Vamitha, V., Rajaram, S. (2015). A multiset based forecasting model for fuzzy time series. Hacettepe Journal of Mathematics and Statistics, Volume: 44 Issue: 4. https://ajindex.com/articles/1403273