Forecasting business cycle with chaotic time series based on neural network with weighted fuzzy membership functions

Soo H. Chai and Joon S. Lim

Chaos, Solitons & Fractals, 2016, vol. 90, issue C, 118-126

Abstract: This study presents a forecasting model of cyclical fluctuations of the economy based on the time delay coordinate embedding method. The model uses a neuro-fuzzy network called neural network with weighted fuzzy membership functions (NEWFM). The preprocessed time series of the leading composite index using the time delay coordinate embedding method are used as input data to the NEWFM to forecast the business cycle. A comparative study is conducted using other methods based on wavelet transform and Principal Component Analysis for the performance comparison. The forecasting results are tested using a linear regression analysis to compare the approximation of the input data against the target class, gross domestic product (GDP). The chaos based model captures nonlinear dynamics and interactions within the system, which other two models ignore. The test results demonstrated that chaos based method significantly improved the prediction capability, thereby demonstrating superior performance to the other methods.

Keywords: Chaotic time series prediction; Time delay coordinate embedding; Neuro-fuzzy network (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:90:y:2016:i:c:p:118-126

DOI: 10.1016/j.chaos.2016.03.037

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