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Autoregressive Modeling of Fuzzy Data Based on the Support Vector Machine
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Elham Ranjbar    , Mohamad Ghasem Akbari * , Reza Zarei |
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Abstract: (183 Views) |
| In the time series analysis, we may encounter situations where some elements of the model are imprecise quantities. One of the most common situations is the inaccuracy of the underlying observations, usually due to measurement or human errors. In this paper, a new fuzzy autoregressive time series model based on the support vector machine approach is proposed. For this purpose, the kernel function has been used for the stability and flexibility of the model, and the constraints included in the model have been used to control the points. In order to examine the performance and effectiveness of the proposed fuzzy autoregressive time series model, some goodness of fit criteria are used. The results were based on one example of simulated fuzzy time series data and two real examples, which showed that the proposed method performed better than other existing methods. |
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| Keywords: Autoregressive model, Fuzzy number, Kernel function, Lagrange function, Support vector machine. |
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Full-Text [PDF 525 kb]
(153 Downloads)
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Type of Study: Research |
Subject:
Fuzzy Statistics
Received: 2024/09/28 | Accepted: 2024/08/31
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