demand_pattern

demand_pattern ¤

Demand pattern events for supply chain timeseries.

Aggregates demand by time period, detects demand spikes, and summarizes seasonal demand patterns.

DemandPatternEvents ¤

DemandPatternEvents(
    dataframe: DataFrame,
    demand_uuid: str,
    *,
    event_uuid: str = "sc:demand",
    value_column: str = "value_double",
    time_column: str = "systime"
)

Bases: Base

Analyze demand patterns from a timeseries demand signal.

Each demand event is a numeric value representing demand quantity at a point in time.

Example usage

analyzer = DemandPatternEvents(df, demand_uuid='order_demand') daily = analyzer.demand_by_period(period='1D') spikes = analyzer.detect_demand_spikes(threshold_factor=2.0) seasonal = analyzer.seasonality_summary(period='1D')

Initialize demand pattern analyzer.

Parameters:

Name	Type	Description	Default
`dataframe`	`DataFrame`	Input DataFrame with timeseries data.	required
`demand_uuid`	`str`	UUID of the demand signal.	required
`event_uuid`	`str`	UUID assigned to generated events.	`'sc:demand'`
`value_column`	`str`	Column containing numeric demand values.	`'value_double'`
`time_column`	`str`	Name of timestamp column.	`'systime'`

demand_by_period ¤

demand_by_period(period: str = '1D') -> pd.DataFrame

Aggregate demand per time period.

Parameters:

Name	Type	Description	Default
`period`	`str`	Pandas offset alias for grouping (e.g. `'1D'`, `'1h'`).	`'1D'`

Returns:

Type	Description
`DataFrame`	DataFrame with columns: period_start, uuid, is_delta,
`DataFrame`	total_demand, avg_demand, peak_demand.

detect_demand_spikes ¤

detect_demand_spikes(
    threshold_factor: float = 2.0, window: str = "1D"
) -> pd.DataFrame

Flag periods where demand exceeds mean + threshold_factor * std.

Parameters:

Name	Type	Description	Default
`threshold_factor`	`float`	Number of standard deviations above the mean to flag as a spike.	`2.0`
`window`	`str`	Pandas offset alias for aggregation period.	`'1D'`

Returns:

Type	Description
`DataFrame`	DataFrame with columns: period_start, uuid, is_delta,
`DataFrame`	demand, baseline_mean, spike_magnitude.

seasonality_summary ¤

seasonality_summary(period: str = '1D') -> pd.DataFrame

Compute demand patterns by day-of-week (for daily) or hour-of-day (for hourly).

When period='1D', groups by day of week (Monday=0 .. Sunday=6). When period is sub-daily (e.g. '1h'), groups by hour of day (0..23).

Parameters:

Name	Type	Description	Default
`period`	`str`	Pandas offset alias. `'1D'` for day-of-week analysis, any sub-daily frequency for hour-of-day analysis.	`'1D'`

Returns:

Type	Description
`DataFrame`	DataFrame with columns: period_label, avg_demand, std_demand,
`DataFrame`	min_demand, max_demand.