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properly link ForecasterParametersSchema in OpenAPI specs
Signed-off-by: Nicolas Höning <nicolas@seita.nl>
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‎flexmeasures/api/v3_0/sensors.py‎

Lines changed: 1 addition & 15 deletions
Original file line numberDiff line numberDiff line change
@@ -1522,21 +1522,7 @@ def trigger_forecast(self, id: int, **params):
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required: true
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content:
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application/json:
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schema:
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type: object
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properties:
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start_date:
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type: string
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format: date-time
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description: Start date of the historical data used for training.
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start_predict_date:
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type: string
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format: date-time
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description: Start date of the forecast period.
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end_date:
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type: string
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format: date-time
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description: End date of the forecast period.
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schema: ForecasterParametersSchema
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example:
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start_date: "2026-01-01T00:00:00+01:00"
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start_predict_date: "2026-01-15T00:00:00+01:00"

‎flexmeasures/ui/static/openapi-specs.json‎

Lines changed: 168 additions & 18 deletions
Original file line numberDiff line numberDiff line change
@@ -1186,24 +1186,7 @@
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"content": {
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"application/json": {
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"schema": {
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"type": "object",
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"properties": {
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"start_date": {
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"type": "string",
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"format": "date-time",
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"description": "Start date of the historical data used for training."
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},
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"start_predict_date": {
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"type": "string",
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"format": "date-time",
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"description": "Start date of the forecast period."
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},
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"end_date": {
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"type": "string",
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"format": "date-time",
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"description": "End date of the forecast period."
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}
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}
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"$ref": "#/components/schemas/ForecasterParameters"
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},
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"example": {
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"start_date": "2026-01-01T00:00:00+01:00",
@@ -5106,6 +5089,173 @@
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],
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"additionalProperties": false
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},
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"ForecasterParameters": {
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"type": "object",
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"properties": {
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"sensor": {
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"type": "integer",
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"description": "ID of the sensor to forecast.",
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"example": 2092
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},
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"future_regressors": {
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"type": "array",
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"description": "Sensor IDs to be treated only as future regressors.",
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"example": [
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2093,
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2094
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],
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"items": {
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"type": "integer"
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}
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},
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"past_regressors": {
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"type": "array",
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"description": "Sensor IDs to be treated only as past regressors.",
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"example": [
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2095
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],
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"items": {
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"type": "integer"
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}
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},
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"regressors": {
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"type": "array",
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"description": "Sensor IDs used as both past and future regressors.",
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"example": [
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2093,
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2094,
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2095
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],
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"items": {
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"type": "integer"
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}
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},
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"model_save_dir": {
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"type": [
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"string",
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"null"
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],
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"default": "flexmeasures/data/models/forecasting/artifacts/models",
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"description": "Directory to save the trained model.",
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"example": "flexmeasures/data/models/forecasting/artifacts/models"
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},
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"output_path": {
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"type": [
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"string",
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"null"
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],
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"description": "Directory to save prediction outputs. Defaults to None (no outputs saved).",
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"example": "flexmeasures/data/models/forecasting/artifacts/forecasts"
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},
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"start_date": {
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"type": [
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"string",
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"null"
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],
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"format": "date-time",
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"description": "Timestamp marking the start of training data. Defaults to train_period before start_predict_date if not set.",
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"example": "2025-01-01T00:00:00+01:00"
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},
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"end_date": {
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"type": [
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"string",
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"null"
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],
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"format": "date-time",
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"description": "End date for running the pipeline.",
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"example": "2025-10-15T00:00:00+01:00"
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},
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"train_period": {
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"type": [
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"string",
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"null"
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],
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"description": "Duration of the initial training period (ISO 8601 format, min 2 days). If not set, derived from start_date and start_predict_date or defaults to P30D (30 days).",
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"example": "P7D"
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},
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"start_predict_date": {
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"type": [
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"string",
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"null"
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],
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"format": "date-time",
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"description": "Start date for predictions. Defaults to now, floored to the sensor resolution, so that the first forecast is about the ongoing event.",
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"example": "2025-01-08T00:00:00+01:00"
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},
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"retrain_frequency": {
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"type": [
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"string",
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"null"
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],
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"description": "Frequency of retraining/prediction cycle (ISO 8601 duration). Defaults to prediction window length if not set.",
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"example": "PT24H"
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},
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"max_forecast_horizon": {
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"type": [
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"string",
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"null"
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],
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"default": "P2D",
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"description": "Maximum forecast horizon. Defaults to 48 hours if not set.",
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"example": "PT48H"
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},
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"forecast_frequency": {
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"type": [
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"string",
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"null"
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],
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"default": "PT1H",
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"description": "How often to recompute forecasts. Defaults to 1 hour.",
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"example": "PT1H"
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},
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"probabilistic": {
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"type": "boolean",
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"default": false,
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"description": "Enable probabilistic predictions if True. Defaults to false.",
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"example": false
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},
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"sensor_to_save": {
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"type": [
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"integer",
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"null"
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],
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"description": "Sensor ID where forecasts will be saved; defaults to target sensor.",
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"example": 2092
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},
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"ensure_positive": {
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"type": [
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"boolean",
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"null"
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],
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"description": "Whether to clip negative values in forecasts. Defaults to None (disabled).",
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"example": true
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},
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"missing_threshold": {
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"type": "number",
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"default": 1.0,
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"description": "Maximum fraction of missing data allowed before raising an error. Defaults to 1.0.",
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"example": 0.1
5238+
},
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"as_job": {
5240+
"type": "boolean",
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"default": false,
5242+
"description": "If True, compute forecasts asynchronously using RQ jobs. Defaults to False.",
5243+
"example": true
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},
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"max_training_period": {
5246+
"type": [
5247+
"string",
5248+
"null"
5249+
],
5250+
"description": "Maximum duration of the training period. Defaults to 1 year (P1Y).",
5251+
"example": "P1Y"
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}
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},
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"required": [
5255+
"sensor"
5256+
],
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"additionalProperties": false
5258+
},
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"TriggerScheduleKwargs": {
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"type": "object",
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"properties": {

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