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How Does FDI Forecast Function Handle Partial Quarters?

Alper Kayaalp

Hi all,

I’m using the built-in forecast function in Oracle Fusion HCM Analytics (FDI) to project quarterly retirement headcount trends.

I’m trying to understand how the forecast handles the current quarter when it’s still in progress. Specifically:
Does the function treat the current (incomplete) quarter as a full data point when generating future forecasts, or is it excluded or adjusted in some way?

It looks like the forecast may be incorporating the current quarter (e.g., 2025 Q2) as a full value, but I’m not completely sure—that’s what I’m trying to confirm. This behavior seems to influence the forecasted values for subsequent quarters in ways that don’t always align with historical trends.

Any guidance on how partial periods are factored into the forecast logic would be greatly appreciated.

Thanks in advance!

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Accepted answers

Philippe Lions-Oracle

Hello @Alper Kayaalp and @Gina DiVenti, thanks for the details and once again, sorry for the delay in responding with this thread.
You are correct, when you use the OAC right-click forecast option, the formula created behind the scenes will only consider time level of the viz itself. Ie, if your X-axis in the Viz is at the quarter level, then the forecast will consider quarters as (full value) inputs. If you have partial data in a quarter, it will ignore this and consider each quarter record to be 'complete'.
The way for you to show what you need is to go into the forecast calculation definition 'manually', that is not using a right click. You can create a custom calculation for forecast exactly the same way as right-click does, but there you can fix the time level. The image below shows a custom calc that forces forecast to go at the month level :

Once you have this calculation, you can then visualize it directly in a viz at the Quarter level for example. See the screenshot below :

Screenshot number 3 shows the forecast at the month level (custom calc) but aggregated at the quarter level.
Let me know if this is not what you are trying to achieve.

Thanks

Alper Kayaalp

Thank you @Philippe Lions-Oracle for the clarification and helpful explanation!

We got our answer — it’s clear now that partial quarters are treated as complete when using the right-click forecast. Appreciate the tip on creating the forecast manually to adjust the time level if needed. That’s really helpful.

Thanks again for your support!

Alper

@Gina DiVenti

All comments

Benjamin Arnulf-Oracle

@Philippe Lions-Oracle for awareness.

Gina DiVenti

@Benjamin Arnulf-Oracle @Philippe Lions-Oracle Do we have any updates on this? It's important for our team to understand how the partial quarters are influencing the forecast models, as this will guide how we structure our dashboards moving forward.

Philippe Lions-Oracle

Hello @Gina DiVenti thanks for your note and sorry for the delay. I am actually not sure of which feature you are using in this case - sorry for maybe missing something : is it the right click forecast option that exists in the Oracle Analytics experience, or are you using a prebuilt calculation that is part of HCM Analytics ? Can you confirm ?
If you are not sure how to answer, can you simply provide a screenshot of your viz/report (where it works) ?

Thanks

Alper Kayaalp

Thanks all @Benjamin Arnulf-Oracle @Philippe Lions-Oracle @Gina DiVenti

I am using built-in statistical feature in HCM, please see my metric calculation and visuals below.

FILTER("HCM - Workforce Core"."Facts - Workforce Events"."Termination Headcount"
using("HCM - Workforce Core"."HR Action"."Action Code" = 'RETIREMENT') )

Philippe Lions-Oracle

Once you have this calculation, you can then visualize it directly in a viz at the Quarter level for example. See the screenshot below :

Screenshot number 3 shows the forecast at the month level (custom calc) but aggregated at the quarter level.
Let me know if this is not what you are trying to achieve.

Thanks

Alper Kayaalp

Thank you @Philippe Lions-Oracle for the clarification and helpful explanation!

Thanks again for your support!

Alper

@Gina DiVenti