EvaluationWindows.build#
- classmethod EvaluationWindows.build(*, periods, dates, l_max, freq_offset, full_axis=False, include_carryover=True)[source]#
Work out the window of each period, and which of its dates are summed.
- Parameters:
- periodssequence of (
pd.Timestamp,pd.Timestamp) Period
(start, end)pairs.- dates
pd.DatetimeIndex All dates from the fitted data.
- l_max
int Evaluation-window half-length, already widened for any mediated path.
- freq_offset
pd.DateOffset Calendar-aware frequency offset.
- include_carryoverbool, default=True
Whether the evaluation range reaches past the period to pick up its carry-out. Independent of the window, which always does: the two lengths do different jobs and collapsing them would change the numbers inside the period too.
- full_axisbool, default=False
Evaluate every period on the complete fitted date axis, for an effect whose value depends on the whole series. Expressed as a window spanning every date rather than as a separate code path, so scenario building, input cutting and period aggregation stay identical: each period still perturbs only its own dates and still sums its increment over its own evaluation mask.
- periodssequence of (
- Returns:
EvaluationWindowsThe windows, and the length they stack to.