{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:42:05Z","timestamp":1777704125501,"version":"3.51.4"},"reference-count":21,"publisher":"SAGE Publications","issue":"2","license":[{"start":{"date-parts":[[2018,7,18]],"date-time":"2018-07-18T00:00:00Z","timestamp":1531872000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2018,8,26]]},"abstract":"<jats:p>Recent years, fuzzy inference systems have been commonly used for time series forecasting. It is well known that fuzzy inference systems can produce good forecasting. Although fuzzy inference systems like adaptive network fuzzy inference system have been preferred by many of researchers, these systems have many of problems. If data set contains many explanatory variables, the number of rules will increase dramatically. Classical fuzzy inference systems need to estimate too many parameters for a reasonable forecasting performance. In this study, a new fuzzy inference system is proposed for time series forecasting. The proposed inference system uses fuzzy c-means method for clustering and pi-sigma neural network for fuzzy modelling. Moreover, the proposed system can generate probabilistic outputs (forecasts) under favour of subsampling block bootstrap method. The performance of the proposed method was investigated by using some data sets. It is understood that the proposed inference system can produce better forecast results.<\/jats:p>","DOI":"10.3233\/jifs-17782","type":"journal-article","created":{"date-parts":[[2018,7,20]],"date-time":"2018-07-20T12:31:33Z","timestamp":1532089893000},"page":"2349-2358","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":7,"title":["A new fuzzy inference system for time series forecasting and obtaining the probabilistic forecasts via subsampling block bootstrap"],"prefix":"10.1177","volume":"35","author":[{"given":"Ufuk","family":"Yolcu","sequence":"first","affiliation":[{"name":"Department of Econometrics, Faculty of Economic and Administrative Sciences, Forecast Research Laboratory, Giresun University, Giresun, Turkey"}]},{"given":"Eren","family":"Bas","sequence":"additional","affiliation":[{"name":"Department of Statistics, Faculty of Arts and Sciences, Forecast Research Laboratory, Giresun University, Giresun, Turkey"}]},{"given":"Erol","family":"Egrioglu","sequence":"additional","affiliation":[{"name":"Department of Statistics, Faculty of Arts and Sciences, Forecast Research Laboratory, Giresun University, Giresun, Turkey"}]}],"member":"179","published-online":{"date-parts":[[2018,7,18]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0019-9958(65)90241-X"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1176344552"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-012-1315-5"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-015-0647-0"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1049\/piee.1974.0328"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1176347265"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2007.12.004"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICNN.1995.488968"},{"key":"e_1_3_2_10_2","first-page":"1981","author":"Bezdek J.C.","unstructured":"BezdekJ.C., Pattern recognition with fuzzy objective function algorithms New York, USA Plenum Press 1981.","journal-title":"Pattern recognition with fuzzy objective function algorithms"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/21.256541"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.3233\/JIFS-16254"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1016\/0165-0114(93)90372-O"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1176348779"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1109\/TFUZZ.2010.2073712"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2010.08.026"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1016\/0165-0114(95)00220-0"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSMCA.2012.2190399"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.1985.6313399"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/CEC.1999.785511"},{"key":"e_1_3_2_21_2","first-page":"I13","article-title":"The Pi-sigma network: An efficient higher-order neural network for pattern classification and function approximation","author":"Shin Y.","year":"1991","unstructured":"ShinY. and GoshJ., The Pi-sigma network: An efficient higher-order neural network for pattern classification and function approximation, In Proceedings of the International Joint Conference on Neural Networks (1991), I13\u2013I-18.","journal-title":"In Proceedings of the International Joint Conference on Neural Networks"},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.3390\/sym9090183"}],"container-title":["Journal of Intelligent &amp; 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