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idea of timedelta based algorithm

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This commit is contained in:
Florian Förster 2025-04-11 12:23:05 +02:00
parent e8f3a7aea8
commit da594fb5ba
1 changed files with 12 additions and 6 deletions
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18
src/delta_barth/analysis/forecast.py
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@ -259,6 +259,12 @@ def _process_sales(
min_date = dates.min()
stride = dopt_basics.datetime.timedelta_from_val(365, TimeUnitsTimedelta.DAYS)
dates = cast(pd.DatetimeIndex, monthly_sum.index)
min_date = dates.min()
# print("dates: ", dates)
# ?? --- new: use monthly basis for time windows
# baseline: 3 years - 36 months
@ -268,12 +274,12 @@ def _process_sales(
# (i for i, date in enumerate(dates) if date >= starting_date), len(dates) - 1
# )
# print("start idx: ", start_index, "length dates: ", len(dates))
# starting_date = datetime.datetime.now() - relativedelta(months=36)
starting_date = dates.max() - relativedelta(months=36)
start_index = next(
(i for i, date in enumerate(dates) if date >= starting_date), len(dates) - 1
)
print("start idx: ", start_index, "length dates: ", len(dates))
starting_date = datetime.datetime.now() - relativedelta(months=12)
# starting_date = dates.max() - relativedelta(months=36)
# start_index = next(
# (i for i, date in enumerate(dates) if date >= starting_date), len(dates) - 1
# )
# print("start idx: ", start_index, "length dates: ", len(dates))
def get_index_date(
dates: pd.DatetimeIndex,