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%.

There have been many studies recently that predict the interactions between genes and reconstruct the gene control network. In this paper, we propose the approach to predict the expression values between the genes of the yeast cell using a neural network based on weighted fuzzy membership function. This neuro fuzzy system makes the exact prediction possible through choosing best rules automatically. Features extracted from original data are used for learning. We extract the five features and they take into account the characteristics of time series by using wavelet transform, Current Position (CP) and time point. The best features to be good for prediction are selected through the Bounded Sum Weight of the weighted fuzzy membership function. The selected features are defuzzified through the Takagi-Sugeno method to calculate the prediction values of original gene expression data. We evaluate mean square error to indicate prediction accuracy of the proposed approach and then compare to the existing algorithm RNN using the neural network. The proposed method outperformed RNN.