An integrated fuzzy time series forecasting system |
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| Authors: | Hao-Tien Liu |
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| Affiliation: | 1. Department of Radiology, Affiliated Hospital of Chengdu University, Chengdu, 610081, China;2. Department of Cardiology, Affiliated Hospital of North Sichuan Medical College, Sichuan 637000, China;3. Department of Radiology, Deyang City People''s Hospital, 618000, China;4. Department of Interventional Radiology, Tenth People''s Hospital of Tongji University, Shanghai 200072, China;5. Department of General Surgery, Affiliated Hospital of Chengdu University, Chengdu 610081, China;6. Department of Urology, Affiliated Hospital of Chengdu University, Chengdu, 610081, China;1. Durham University, United Kingdom of Great Britain and Northern Ireland;2. University of Glasgow, United Kingdom of Great Britain and Northern Ireland;1. Technical University of Cluj-Napoca, Romania;2. Babes-Bolyai University of Cluj-Napoca, Romania;3. Université Nice Sophia-Antipolis, France;1. Radiation Oncology, Nayati Healthcare and Research Centre, Block 3A, 3rd Floor, DLF Corporate Park, DLF City, Gurgaon, Haryana 122002, India;2. Biostatistics, Nayati Healthcare and Research Centre, Block 3A, 3rd Floor, DLF Corporate Park, DLF City, Gurgaon, Haryana 122002, India |
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| Abstract: | A number of fuzzy time series models have been designed and developed during the last decade. One problem of these models is that they only provide a single-point forecasted value just like the output of the crisp time series methods. In addition, these models are suitable for forecasting stationary or trend time series, but they are not appropriate for forecasting seasonal time series. Hence, the objective of this study is to develop an integrated fuzzy time series forecasting system in which the forecasted value will be a trapezoidal fuzzy number instead of a single-point value. Furthermore, this system can effectively deal with stationary, trend, and seasonal time series and increase the forecasting accuracy. Two numerical data sets are selected to illustrate the proposed method and compare the forecasting accuracy with four fuzzy time series methods. The results of the comparison show that our system can produce more precise forecasted values than those of four methods. |
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