I'm currently testing code against dataframes with various pandas-supported frequencies and using generate_series to create these dataframes. However, I noticed that only daily and monthly frequencies are supported:
The limitation appears to be related to how season is defined for each frequency, which is necessary for simulating seasonal behavior as seen in this snippet from data.py:
For frequencies not accounted for in seasonalities, I suggest we could raise a warning and then populate the y variable with a random distribution. This approach could be particularly useful when the model's performance isn't being evaluated, and we're more interested in the structure of the dataframe for a specific frequency.
I'm currently testing code against dataframes with various pandas-supported frequencies and using
generate_series
to create these dataframes. However, I noticed that only daily and monthly frequencies are supported:The limitation appears to be related to how
season
is defined for each frequency, which is necessary for simulating seasonal behavior as seen in this snippet fromdata.py
:https://github.com/Nixtla/utilsforecast/blob/2812ef011905e202fa801b6a28f7b43f892e2d78/utilsforecast/data.py#L90
For frequencies not accounted for in
seasonalities
, I suggest we could raise a warning and then populate they
variable with a random distribution. This approach could be particularly useful when the model's performance isn't being evaluated, and we're more interested in the structure of the dataframe for a specific frequency.