Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - A new fuzzy inference system for time series forecasting and obtaining the probabilistic forecasts via subsampling block bootstrap
AU - Yolcu, Ufuk
AU - Bas, Eren
AU - Egrioglu, Erol
PY - 2018/8/26
Y1 - 2018/8/26
N2 - 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.
AB - 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.
KW - fuzzy c-means
KW - Fuzzy inference systems
KW - particle swarm optimization
KW - probabilistic forecasts
KW - subsampling block bootstrap
U2 - 10.3233/JIFS-17782
DO - 10.3233/JIFS-17782
M3 - Journal article
AN - SCOPUS:85053336439
VL - 35
SP - 2349
EP - 2358
JO - Journal of Intelligent and Fuzzy Systems
JF - Journal of Intelligent and Fuzzy Systems
SN - 1064-1246
IS - 2
ER -