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%.

This study presents a business cycle forecasting model using economic indicators based on Fuzzy Interactive Naive Bayesian (FINB) network. FINB classifier is a modified model to enhance the classification capacity by weakening the conditional independence of naive Bayesian network. In particular, it facilitates the construction of an interaction network map consisting of leading indexes, thereby clarifying the degree of influence of a certain index to and from other indexes. The experimental results of the final interaction network map provide valuable information hidden in the prediction mechanism. Particularly, under the changing economic environment in the wake of the global financial crisis of 2008, future economic situations are hard to predict owing to the complexity of financial systems in which increased variables and the heightened interdependencies between them are interwoven within the system. These factors in turn aggravated the fragility of the financial system as a whole, resulting in increased uncertainty in predicting the outlook of the world economy. The proposed interaction network map described here will provide new insight into how the financial mechanism operates, new information pinpointing the main factors of the business cycle.