Neural fuzzy regression modelling for forecasting

Abstract

Recently statistical model especially forecasting model has developed to soft model. The model is more computerize in line to computer development and it is not based on strict rules, such as it has to fulfil classical or soft assumption. The model is called as soft statistical model. By using soft model and soft assumption, there are many models can be constructed, such as Artificial Neural Network (ANN) or Multi Layers Perceptions (MLP). There are three layers in ANN, it is called input, hidden and output layer. The optimum weight of each layer is processed using back propagation approach. In this research, ANN model – especially Neural Fuzzy Regression (NFR) model – is applied to find best forecasting model, specifically forecasting model of the stock price. The data is the stock price of a mining sector emitted and the exchange rate US$ to IDR from January 2015 until February 2019. The Data is collected from publication of Indonesia Stock Exchange and Indonesia Central Bank. The stock price shows positive trends recently and there is a correlation between the stock price and the exchange rate. Based on autocorrelation function, there are four previous data that have significant relationship with the current data. NFR model has five nodes in input layer (four lag time and exchange rate), some nodes in hidden layer and a node in output layer. The best model is model with five nodes as input, seven nodes in hidden layer and an output. The model has accuracy of MSE 34.0850, MAPE 2.9026, and MAD 28.7377.