Allows for an assessment of the existence and nature of any seasonality in the series. All the monthly values are plotted in chronological order with each month on the horizontal axis. The years represent the 10 separate years of data that in the time series. We can see that our data has seasonal spikes during the summer and winter months.
Allows for an assessment of whether the underlying nature of the time series varies across seasons. Our bookings series shows that January, February and August have the greatest deviations.
Determine the nature of any underlying autocorrelation and in assessing the possible needs in terms of data differencing for the creation of an ARIMA model. The lag refers to the number of periods behind the most recent period and the correlation coefficient is the vertical axis.
Determine the nature of any underlying autocorrelation and in assessing the possible needs in terms of data differencing for the creation of an ARIMA model. The lag refers to the number of periods behind the most recent period and the correlation coefficient is the vertical axis.
A visual examination of the original data, the trend in the data, the seasonality in the data, and information about the residuals once seasonality and trend have been take into account.
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Stock Three Example.xlsx
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Seasonal Plot