ARIMA is one of most popular time series forecasting method from econometrics. ARIMA is an acronym that stands for AutoRegressive Integrated Moving Average.
It is a very powerful method as it clearly caters to a suite of standard structures in time series data.
Next, let’s take a look at how we can use the ARIMA model in SAP IBP. We will start by loading a simple time-series data.
Upload the sales data in SAP IBP using the Data integration app or CPI.
Create the forecast model using the “Manage forecast model” App.
From planning view or in background run statistical forecasting model selecting Auto - ARIMA/SARIMA.
View detailed log for error measures in the business log.
A line plot created showing the Ex-post forecast Quantity(orange) to Actual Sales Quantity(blue) and also showing expected forecast(red).
Conclusion:
It is discovered how standard structures in time series data are captured in Ex-post forecast and the Expected future forecast.
A pattern of growth/decline in the data is accounted, the rate of change of the growth/decline in the data is accounted and noise between consecutive time points is accounted.
References
https://help.sap.com/viewer/feae3cea3cc549aaa9d9de7d363a83e6/1908/en-US/c9b4470ccc0b47b7bed31d18c76f...
https://ademos.people.uic.edu/Chapter23.html
It is a very powerful method as it clearly caters to a suite of standard structures in time series data.
Next, let’s take a look at how we can use the ARIMA model in SAP IBP. We will start by loading a simple time-series data.
Upload the sales data in SAP IBP using the Data integration app or CPI.
Create the forecast model using the “Manage forecast model” App.
From planning view or in background run statistical forecasting model selecting Auto - ARIMA/SARIMA.
View detailed log for error measures in the business log.
A line plot created showing the Ex-post forecast Quantity(orange) to Actual Sales Quantity(blue) and also showing expected forecast(red).
Conclusion:
It is discovered how standard structures in time series data are captured in Ex-post forecast and the Expected future forecast.
A pattern of growth/decline in the data is accounted, the rate of change of the growth/decline in the data is accounted and noise between consecutive time points is accounted.
References
https://help.sap.com/viewer/feae3cea3cc549aaa9d9de7d363a83e6/1908/en-US/c9b4470ccc0b47b7bed31d18c76f...
https://ademos.people.uic.edu/Chapter23.html
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