Budi Warsito

Application of Wavelet Neuro-Fuzzy System (WNFS) method for stock forecasting

Forecasting is a very important element in decision making, because the effectiveness of a decision, generally depends on several factors that we cannot see when the decision was taken. In this study, the Wavelet Neuro-Fuzzy System (WNFS) model that combines wavelet transformation and neuro-fuzzy techniques is applied to forecast daily closing stock price data of BMRI.JK. The observed daily stock price data are decomposed into some sub-series components by maximal overlap discrete wavelet transform (MODWT), then the appropriate sub-series that have higher correlation to the real data are used as inputs of the neuro-fuzzy model for daily forecasting stock price for three days in advance. The neuro-fuzzy model is begun with determining the membership value of each data using Fuzzy C-Means, followed by fuzzy inference procedure of the Sugeno model. The result shows that the presence of wavelet input in Neuro-Fuzzy System, can provide optimal prediction in daily stock price data, with small error value of predicted result. This would be helped investors or economists to produce meaningful information in either buy or sell a stock.

Classification of Company Performance using Weighted Probabilistic Neural Network

Classification of company performance can be judged by looking at its financial status, whether good or bad state. Classification of company performance can be achieved by some approach, either parametric or non-parametric. Neural Network is one of non-parametric methods. One of Artificial Neural Network (ANN) models is Probabilistic Neural Network (PNN). PNN consists of four layers, i.e. input layer, pattern layer, addition layer, and output layer. The distance function used is the euclidean distance and each class share the same values as their weights. In this study used PNN that has been modified on the weighting process between the pattern layer and the addition layer by involving the calculation of the mahalanobis distance. This model is called the Weighted Probabilistic Neural Network (WPNN). The results show that the companys performance modeling with the WPNN model has a very high accuracy that reaches 100%.

Cascade Forward Neural Network for Time Series Prediction

Cascade-forward neural network is a class of neural network which is similar to feed-forward networks, but include a connection from the input and every previous layer to following layers. In a network which has three layers, the output layer is also connected directly with the input layer beside with hidden layer. As with feed-forward networks, a two-or more layer cascade-network can learn any finite input-output relationship arbitrarily well given enough hidden neurons. Cascade-forward neural network can be used for any kind of input to output mapping. The advantage of this method is that it accommodates the nonlinear relationship between input and output by not eliminating the linear relationship between the two. In this study, we apply the network in time series field. The optimal architecture was determined computationally by using incremental search method in both input and hidden units. The simple one was built first, and then the more complex is constructed by adding the units one by one. The optimal one is chosen then by using the mean square error criteria.