[date features]: dayofweek_cat - day of week as a one hot encoding feature about mlforecast HOT 4 OPEN

Hey @nelsoncardenas, thanks for using mlforecast and for the detailed report. I think the easiest way to achieve this is with a scikit-learn pipeline. Here's an example:

import pandas as pd
from mlforecast import MLForecast
from mlforecast.utils import generate_daily_series
from sklearn.compose import ColumnTransformer
from sklearn.linear_model import LinearRegression
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import OneHotEncoder

series = generate_daily_series(1, min_length=7, max_length=7)
model = make_pipeline(
    ColumnTransformer(
        [('encoder', OneHotEncoder(drop='first'), ['dayofweek'])],
        remainder='passthrough'
    ),
    LinearRegression()
)
fcst = MLForecast(models={'lr': model}, freq="D", date_features=["dayofweek"])
fcst.fit(series)
print(fcst.models_['lr'].named_steps['linearregression'].n_features_in_)  # 6

The available attributes are:

• pandas: the ones listed under the "Attributes" section here.
• polars: most of the ones here.

If you have time and would like to do it we'd appreciate a PR that explicitly lists the supported ones.

from mlforecast.

Thank you @jmoralez I'd like to help with that PR.

What would be the suggested steps?

from mlforecast.

I think you could add two lists (one for pandas and one for polars) in the nbs/core.ipynb notebook. We have this file with some contributing guidelines, but the first step should be to fork this repository and work on your fork instead (I'll fix that soon). Let me know if you have any questions.

from mlforecast.

@jmoralez Thank you. During the week I will dedicate some free time to it.

from mlforecast.