A new hybrid method for time series forecasting: AR-ANFIS

Sarica B., EĞRİOĞLU E., AŞIKGİL B.

NEURAL COMPUTING & APPLICATIONS, vol.29, no.3, pp.749-760, 2018 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Volume: 29 Issue: 3
  • Publication Date: 2018
  • Doi Number: 10.1007/s00521-016-2475-5
  • Journal Name: NEURAL COMPUTING & APPLICATIONS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.749-760
  • Keywords: Adaptive network fuzzy inference system, Autoregressive model, Fuzzy inference system, Time series, Particle swarm optimization, Fuzzy C-Means, NEURAL-NETWORK, FUZZY, SUPPORT, SYSTEMS, MODEL, LOAD
  • Marmara University Affiliated: Yes

Abstract

In this study, a new hybrid forecasting method is proposed. The proposed method is called autoregressive adaptive network fuzzy inference system (AR-ANFIS). AR-ANFIS can be shown in a network structure. The architecture of the network has two parts. The first part is an ANFIS structure and the second part is a linear AR model structure. In the literature, AR models and ANFIS are widely used in time series forecasting. Linear AR models are used according to model-based strategy. A nonlinear model is employed by using ANFIS. Moreover, ANFIS is a kind of data-based modeling system like artificial neural network. In this study, a linear and nonlinear forecasting model is proposed by creating a hybrid method of AR and ANFIS. The new method has advantages of data-based and model-based approaches. AR-ANFIS is trained by using particle swarm optimization, and fuzzification is done by using fuzzy C-Means method. AR-ANFIS method is examined on some real-life time series data, and it is compared with the other time series forecasting methods. As a consequence of applications, it is shown that the proposed method can produce accurate forecasts.