Time Series Analysis: An application of ARIMA model in stock price forecasting

Abstract

Time series models have been the foundation of the analysis of a process over a long period of time and their applications are manifold, including sales forecasting, index forecasting etc. In decisions involving uncertainties, time series models are noted as one of the most effective ways of making predictions. Among the many models, the autoregressive integrated moving average (ARIMA) models have been especially popular in time series prediction. This paper presents extensive process of building stock price predictive model using the ARIMA method. Stock data of Apple Inc. obtained from Yahoo! Finance are used. Results revealed that the ARIMA model has a strong potential for short-run forecast and is competitive with other prediction methods in guiding investment decisions.