Advances in Time Series Forecasting

Volume: 1

Comparison of Feed Forward and Elman Neural Networks Forecasting Ability: Case Study for IMKB

Author(s): Erol Eǧrioǧlu, Cagdas Hakan Aladag and Ufuk Yolcu

Pp: 11-17 (7)

DOI: 10.2174/978160805373511201010011

* (Excluding Mailing and Handling)


In recent years, artificial neural networks (ANN) have been widely used in real life time series forecasting. Artificial neural networks can model both linear and curvilinear structure in time series. Most of the conventional methods used in the analysis of time series are linear structure and fail to analyze non-linear time series. In conventional time series methods such as threshold autoregressive, bilinear model, which are used in non-linear time series modeling, a particular curvilinear model pattern is needed. Artificial neural network is a method based on data and does not require a model pattern. With its activation function, it provides flexible non-linear modeling. Additionally, when compared with conventional methods, successful results are obtained in forecasting time series via artificial neural networks in the literature. In this study, feed forward and feedback artificial neural networks which are widely used for time series forecasting were applied to Istanbul Stock Exchange Market (IMKB) time series and forecasting performances were evaluated.

Keywords: Artificial neural networks, Feed forward, Feedback, Forecasting, Time series.

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