The time series data undertaken for analysis is about Nike quarter revenue ranging from the year 2000 to 2017. From this series analysis, we can analyze how the revenue is varying every year and quarter and analyze the revenue for further years.

The data we have considered includes a trend and seasonality; I have used ACF and PACF plots to obtain stationary data by performing moving average method. The seasonality is decomposed by using decomposition method, to obtain complete stationary data. This data is now used to fit for ARIMA model.

The final forecast using ARIMA for next five years is performed with confidence interval to be 95%.