Also, I think I found a bug in the utilsforecast.preprocessing.fill_gaps() method. When using weekly frequency, data is lost unless specific weekly bucketing is used:

from utilsforecast import preprocessing as pr
import pandas as pd
import numpy as np

df = pd.DataFrame(
    {
        'unique_id': [0, 0, 0, 1, 1],
        'ds': pd.to_datetime(['2018-11-11', '2018-11-18', '2018-12-02', '2018-11-11', '2018-12-02']),
        'y': np.arange(5),
    }
)
df1 = pr.fill_gaps(
    df,
    freq='W',
)
print(df)
print(df1)

##### OUTPUT ######

   unique_id         ds  y
0          0 2018-11-11  0
1          0 2018-11-18  1
2          0 2018-12-02  2
3          1 2018-11-11  3
4          1 2018-12-02  4
   unique_id         ds   y
0          0 2018-11-08 NaN
1          0 2018-11-15 NaN
2          0 2018-11-22 NaN
3          0 2018-11-29 NaN
4          1 2018-11-08 NaN
5          1 2018-11-15 NaN
6          1 2018-11-22 NaN
7          1 2018-11-29 NaN

This is happening because the numpy definition for week is bucketed starting on Thursdays:

np.datetime64(pd.to_datetime("2018-11-11"),'W')

##### OUTPUT ######

numpy.datetime64('2018-11-08')

If I comment out the last two lines of _determine_bound in the fill_gaps method, the problem is resolved:

def fill_gaps(
    df: pd.DataFrame,
    freq: str = 'W',
    start: str = "per_serie",
    end: str = "global",
    id_col: str = "unique_id",
    time_col: str = "ds",
) -> pd.DataFrame:
    delta = np.timedelta64(1, freq) if isinstance(freq, str) else freq
    times_by_id = df.groupby(id_col)[time_col].agg(["min", "max"])
    starts = _determine_bound(start, freq, times_by_id, "min")
    ends = _determine_bound(end, freq, times_by_id, "max") + delta
    sizes = ((ends - starts) / delta).astype(np.int64)
    times = np.concatenate(
        [np.arange(start, end, delta) for start, end in zip(starts, ends)]
    )
    if isinstance(freq, str):
        times = times.astype("datetime64[ns]", copy=False)
    uids = np.repeat(times_by_id.index, sizes)
    idx = pd.MultiIndex.from_arrays([uids, times], names=[id_col, time_col])
    return df.set_index([id_col, time_col]).reindex(idx).reset_index()
    
def _determine_bound(bound, freq, times_by_id, agg) -> np.ndarray:
    if bound == "per_serie":
        out = times_by_id[agg].values
    else:
        # the following return a scalar
        if bound == "global":
            val = getattr(times_by_id[agg].values, agg)()
            if isinstance(freq, str):
                val = np.datetime64(val)
        else:
            if isinstance(freq, str):
                # this raises a nice error message if it isn't a valid datetime
                val = np.datetime64(bound)
            else:
                val = bound
        out = np.full(times_by_id.shape[0], val)
    #if isinstance(freq, str):
        #out = out.astype(f"datetime64[{freq}]")
    return out

df = pd.DataFrame(
    {
        'unique_id': [0, 0, 0, 1, 1],
        'ds': pd.to_datetime(['2018-11-11', '2018-11-18', '2018-12-02', '2018-11-11', '2018-12-02']),
        'y': np.arange(5),
    }
)
df1 = fill_gaps(
    df,
    freq='W',
)
print(df)
print(df1)

##### OUTPUT #####
   unique_id         ds  y
0          0 2018-11-11  0
1          0 2018-11-18  1
2          0 2018-12-02  2
3          1 2018-11-11  3
4          1 2018-12-02  4
   unique_id         ds    y
0          0 2018-11-11  0.0
1          0 2018-11-18  1.0
2          0 2018-11-25  NaN
3          0 2018-12-02  2.0
4          1 2018-11-11  3.0
5          1 2018-11-18  NaN
6          1 2018-11-25  NaN
7          1 2018-12-02  4.0

However, with this change problems occur when the weekly bucketing of the data is not precise (i.e. if I accidentally put '2018-12-01' in it would error)

Thanks for raising this! Indeed we need to figure out a way to deal with weeks that aren't fixed on thursdays, I'll work on a fix

Hey just saw this. Seems like the branch has already been merged but yep, new version seems to work! Thanks for fixing this!

Thanks for reporting! I've added some more thorough tests and hopefully it won't happen again