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A fuzzy non-parametric time series model based on fuzzy data | ||
| Iranian Journal of Fuzzy Systems | ||
| مقاله 6، دوره 19، شماره 1، فروردین و اردیبهشت 2022، صفحه 61-72 اصل مقاله (1.77 MB) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22111/ijfs.2022.6551 | ||
| نویسندگان | ||
| G. Hesamian* ؛ F. Torkian؛ M. Yarmohammadi | ||
| Department of Statistics, Payame Noor University, Tehran 19395-3697, Iran | ||
| چکیده | ||
| Parametric time series models typically consists of model identification, parameter estimation, model diagnostic checking, and forecasting. However compared with parametric methods, nonparametric time series models often provide a very flexible approach to bring out the features of the observed time series. This paper suggested a novel fuzzy nonparametric method in time series models with fuzzy observations. For this purpose, a fuzzy forward fit kernel-based smoothing method was introduced to estimate fuzzy smooth functions corresponding to each observation. A simple optimization algorithm was also suggested to evaluate optimal bandwidths and autoregressive order. Several common goodness-of-fit criteria were also extended to compare the performance of the proposed fuzzy time series method compared to other fuzzy time series model based on fuzzy data. Furthermore, the effectiveness of the proposed method was illustrated through two numerical examples including a simulation study. The results indicate that the proposed model performs better than the previous ones in terms of both scatter plot criteria and goodness-of-fit evaluations. | ||
| کلیدواژه‌ها | ||
| Goodness-of-fit criteria؛ additive؛ kernel function؛ fuzzy response؛ fuzzy time series؛ fuzzy smooth function | ||
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آمار تعداد مشاهده مقاله: 83 تعداد دریافت فایل اصل مقاله: 160 |
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