Title :
Stock Price Prediction: Comparison of Arima and Artificial Neural Network Methods - An Indonesia Stock´s Case
Author :
Wijaya, Yohanes Budiman ; Kom, S. ; Napitupulu, Togar Alam
Author_Institution :
Manajemen Sistem Informasi, Universitas Bina Nusantara, Jakarta, Indonesia
Abstract :
Neural Network is a network that resembles a human brain tissue, which may infer a result based on the facts or experience that happened. Many applications have implemented neural network. In this thesis, we compared the stock forecasting result of ANTM (PT Aneka Tam bang) using Artificial Neural Network and ARIMA. ARIMA is a technique of time-series forecasting, which means forecast based on the existing pattern. The results of the study showed that forecasting using Artificial Neural Network method has higher accuracy value than the results with ARIMA method.
Keywords :
autoregressive moving average processes; neural nets; stock markets; time series; ARIMA method; Indonesia stock; artificial neural network; autoregressive integrated moving average; human brain tissue; stock forecasting; stock price prediction; time-series forecasting; Artificial neural networks; Biological system modeling; Data models; Forecasting; Instruments; Neurons; Predictive models; ARIMA; Artificial Neural Network; stock forecasting; time-series;
Conference_Titel :
Advances in Computing, Control and Telecommunication Technologies (ACT), 2010 Second International Conference on
Conference_Location :
Jakarta
Print_ISBN :
978-1-4244-8746-2
Electronic_ISBN :
978-0-7695-4269-0
DOI :
10.1109/ACT.2010.45
