We read every piece of feedback, and take your input very seriously.
To see all available qualifiers, see our documentation.
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Hi, I saw someone in the slack asking if there is a way to handle temporal hierarchies. So I'm opening this feature request, if it can be done.
Thanks very much
For example, I have daily data, I would like to aggregate it weekly and monthly. Forecast each temporal level and reconciliate.
The text was updated successfully, but these errors were encountered:
Hey @NasreddineD. I believe you can do this by adding the columns and defining the spec, e.g.
df['month'] = df['ds'].dt.month.astype(str) df['week'] = df['ds'].dt.isocalendar()['week'].astype(str) spec = [['month', 'week']] aggregate(df, spec)
Sorry, something went wrong.
Hi @jmoralez,
My request was referring to this request (https://nixtlacommunity.slack.com/archives/C05CAFHU8TX/p1695115672015549?thread_ts=1695115672.015549&cid=C05CAFHU8TX) Answered by @kdgutier
Thanks
No branches or pull requests
Description
Hi,
I saw someone in the slack asking if there is a way to handle temporal hierarchies.
So I'm opening this feature request, if it can be done.
Thanks very much
Use case
For example, I have daily data, I would like to aggregate it weekly and monthly.
Forecast each temporal level and reconciliate.
The text was updated successfully, but these errors were encountered: