A Novel Methodology for Forecasting Business Cycles Using ARIMA and Neural Network with Weighted Fuzzy Membership Functions

Abstract

Economic forecasting is crucial since it benefits many different parties, such as governments, businesses, investors, and the general public. This paper presents a novel methodology for forecasting business cycles using an autoregressive integrated moving average (ARIMA), a popular linear model in time series forecasting, and a neural network with weighted fuzzy membership functions (NEWFM) as a forecasting model generator. The study used a dataset that included seven components of the leading composite index, which is used to predict positive or negative trends in several economic sectors before the GDP is compiled. The preprocessed time series data comprising the leading composite index using ARIMA were used as input vectors for the NEWFM to predict comprehensive business fluctuations. The prediction capability significantly improved through the duplicated refining process of the dataset using ARIMA and NEWFM. The combined ARIMA and NEWFM techniques exceeded ARIMA in both classification and prediction, yielding an accuracy of 91.61%.

Keywords

• time series prediction
• neural fuzzy networks
• autoregressive integrated moving average
• NEWFM
• gross domestic product (GDP)