ORCID "Tak, Nihat/0000-0001-8796-5101" WoS İndeksli Yayınlar Koleksiyonu için listeleme
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An adaptive forecast combination approach based on meta intuitionistic fuzzy functions
Tak, Nihat; Egrioğlu, Erol; Baş, Eren; Yolcu, Ufuk (Ios Press, 2021)Intuitionistic meta fuzzy forecast combination functions are introduced in the paper. There are two challenges in the forecast combination literature, determining the optimum weights and the methods to combine. Although ... -
Dating currency crises and designing early warning systems: Meta-possibilistic fuzzy index functions
Tak, Nihat; Gök, Adem (Wiley, 2020)In order to analyse the currency crises in Turkey over the period of January 1990 and October 2019, we first dated currency crises with meta-possibilistic fuzzy index functions. Then, we determined the significant predictors ... -
Forecast combination with meta possibilistic fuzzy functions
Tak, Nihat (Elsevier Science Inc, 2021)There are many methods to obtain accurate forecasts for time series data in the literature. It is imperative to find an appropriate method with the correct assumptions for a given data set and circumstances. However, the ... -
META FUZZY INDEX FUNCTIONS
Tak, Nihat (Ankara Univ, Fac Sci, 2020)Meta-analysis was introduced to aggregate the findings of different primary studies in statistical aspects. However, in the proposed study, the term meta is used to aggregate different models for a specific topic with the ... -
Type-1 possibilistic fuzzy forecasting functions
Tak, Nihat (Elsevier, 2020)Type-1 Fuzzy Functions (T1FFs) were developed by Turksen as an alternative fuzzy inference system (FIS) and have been commonly used in forecasting problems. The main advantages of T1FFs are that they are free of rules and ... -
Type-1 recurrent intuitionistic fuzzy functions for forecasting
Tak, Nihat (Pergamon-Elsevier Science Ltd, 2020)In this study, a novel forecasting method that employs intuitionistic fuzzy c-means clustering and a grey wolf optimizer in recurrent type-1 fuzzy functions (R-T1FFs) is introduced. R-T1FFs, which adapt to the moving average ...