Winter Historical Inventory Data has been used for analysis in the data. In order to assess the trend of the given output, multiple approaches can be applied depending upon the future prospects. Since the data given involves time factor which includes seasonal and dimensional changes in productivity level. Autoregressive Integrated Moving Average Model ARIMA considers equalize variances for the data. It also requires stationary series data which makes the expected value of series independent of time (Armstrong, 2001).
In some situations, non-stationary data can be converted to stationary data by removing the seasonal differences ...