It is often the case that managers and social scientists are called to deal with time series. Time series analysis usually involves a study of the components of the time series and finding models that permit statistical inferences and predictions. ARIMA models are, in theory, the most general class of models for forecasting a time series. The commonly known Box-Jenkins approach to ARIMA model building is an iterative process. To facilitate the iterative process and to relieve the boredom of computational errands, we have developed an assistor for building ARIMA models. The assistor is implemented in Java with embedded R for statistical functions. With the help of the assistor ARIMA models for time series are few clicks away, thus enabling users to focus their efforts on the decision problems at hand.

Abstract-The production of business analytics inevitably involves applying some statistical procedures to a data set related to the decision problem for a particular scenario. In light of the sheer volume of the data set, it is very often that the computation chores rely very much upon statistical software or others alike. Most of the software for producing business analytics on the market is proprietary with little openness. In this research we exploit the idea of statistical analysis as a service on the cloud and develop a web-based multiple regressor for data analysis. To maintain the portability of the multiple regressor to a maximum degree, the regressor is implemented using Java technology on an open platform with R providing analytic functions. Results of this research have a potential to address issues such as the staggering cost of license fees of commercial packages for SMBs and the geography limitations on where the analytic service is available. This research also reveals the promising future of open-source ware in business applications.

This study adopts popular back-propagation neural network to make one-period-ahead prediction of the stock price. A model based on Taylor series by using both fundamental and technical indicators EPS and MACD as input data is built for an empirical study. Leading Taiwanese companies in non-hi-tech industry such as Formosa Plastics, Yieh Phui Steel, Evergreen Marine, and Chang Hwa Bank are picked as targets to analyze their reasonable prices and moving trends. The performance of this model shows remarkable return and high accuracy in making long/short strategies.