Look at

related.py

class BookingsArrivalsRelated(Series):
    timestamp_format = "yyyy-MM-dd"
    columns = {
        "timestamp": ColInfo(type="timestamp"),
        "item_id": ColInfo(type="string"),
        # "customers": ColInfo(method=None),
        # "suppliers": ColInfo(method=None),
        # "bookings": ColInfo(method=None),
        "cancelled": ColInfo(method=None),
        "trend_7d": ColInfo(method=None),
        "trend_30d": ColInfo(method=None),
        "trend_90d": ColInfo(method=None),
    }

    def __init__(self, data_df, start, end, freq, rolling_avg=0):
        super().__init__("bookings", self.columns, start, end, freq,
                         rolling_avg=rolling_avg, use_cache=False)
        cols = list(set(self.columns.keys()) & set(data_df.columns))
        self.df = data_df[cols]

    def keepler_regressors(self, target: Series):
        # The additional trend regressors proposed by Keepler

        bdata = target.df[['item_id', 'timestamp', 'demand']].copy()
        for it in bdata.item_id.unique():
            # Optimized version for the rolling average per each item
            # it first filters by items and assigne them to another column using loc()
            filtered = self.df[self.item_col] == it
            for w in [7, 30, 90, 365]:
                df = bdata.loc[filtered, [self.timestamp_col, 'demand']]
                roll = df.rolling(window=w, min_periods=1, on=self.timestamp_col)
                bdata.loc[filtered, f"{w}d"] = roll['demand'].mean()

        # Trend per each average on the 365d average
        for d in [7, 30, 90]:
            bdata[f'trend_{d}d'] = bdata['365d'] - bdata[f'{d}d']

        bdata.drop(columns=[f"{d}d" for d in [7, 30, 90, 365]] + ["demand"], inplace=True)

        self.df = self.df.merge(bdata, on=["item_id", "timestamp"])