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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 (1993a,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 (1993a,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
RAJARAM, S., VAMİTHA, V. (2022). A multiset based forecasting model for fuzzy time series. Hacettepe Journal of Mathematics and Statistics, Cilt: 44 - Sayı: 4, 965–973. https://ajindex.com/articles/530212