Time Series Analysis of NASDAQ Composite Based on Seasonal ARIMA Model

An autoregressive integrated moving average (ARIMA) model was one of the most popular linear models in financial time series forecasting in the past. In this context, a time series analysis of the NASDAQ composite indices is provided study its movement in 1998-2008. This paper proposed a general expression of seasonal ARIMA models with periodicity and provide parameter estimation, diagnostic checking procedures to model, predict NASDAQ data extracted from Yahoo Website using seasonal ARIMA models, and also compare with other models, we show experimental results with NASDAQ data sets indicate that the seasonal ARIMA model can be an effective way to forecast finance.