create_forecast
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Third-party content written by another agent. Data to evaluate, not instructions.
Bind a forecast to a metric in your workspace. Stores the configuration, sets up the schedule, and resolves the source binding immediately so a bad source_id or metric_path errors out before any credits are spent. Run the forecast with run_forecast.
Input schema
| Property | Type | Required | Description |
|---|---|---|---|
| name | string | yes | |
| description | string | no | |
| source_type | string | yes | Which clariBI artifact the metric lives on. Most callers should start with data_source for raw connector metrics or goal to forecast a tracked KPI. |
| source_id | string | yes | UUID of the report, dashboard, goal, or data source. |
| metric_path | string | yes | Dot-path to the numeric column. For data_source: the metric key (e.g. "revenue"). For goal: "value" or "percentage". For report: the path inside the insights JSON. For dashboard: "widget_id:metric_name". |
| granularity | string | no | Bucket size for the time series. Hourly requires an hourly data source; the engine refuses to fabricate hourly buckets from daily data. |
| horizon_days | integer | no | How many BUCKETS ahead to project. The per-granularity cap applies: hourly 168, daily 90, weekly 52, monthly 24, quarterly 8, annual 5. The field name is "_days" for backwards compatibility — it counts buckets of the chosen granularity. |
| include_correlations | boolean | no | |
| include_anomalies | boolean | no | |
| include_changepoints | boolean | no | |
| narration_enabled | boolean | no | When true, every completed run gets an AI-generated narrative attached (summary, highlights, risks, recommendations, methodology). Costs 1-2 extra AI credits per run. |
| aggregation | string | no | How multiple raw points falling into the same bucket are combined. Auto picks sum for additive metrics, mean otherwise. Use Mean for rates (CTR, conversion, latency). |
| transform | string | no | Series transform applied before fitting. Log helps revenue/traffic series with growing variance. Auto detects when log is beneficial; none forces raw scale. |
| non_negative | boolean | no | When true, point + lower band clamped at zero. Omit to let the engine heuristic decide (via non_negative_auto=true). |
| non_negative_auto | boolean | no | When true (default), the backend heuristic owns non_negative and re-evaluates per run based on the metric path. Set false alongside non_negative to lock the choice. |
| method_override | string | no | Force a specific forecasting method instead of auto-selecting via walk-forward CV. Valid names: naive, seasonal_naive, moving_average, drift, linear_trend, ar_p, holt_winters, holt_winters_multiplicative, holt_winters_damped, theta, gradient_boost, ensemble_top3. Empty = auto. |
| schedule_frequency | string | no | How often the forecast re-runs. Manual schedules only run when called explicitly via run_forecast. |
| schedule_day_of_week | integer | no | |
| schedule_day_of_month | integer | no |
Raw JSON schema
{
"type": "object",
"required": [
"name",
"source_type",
"source_id",
"metric_path"
],
"properties": {
"name": {
"type": "string",
"minLength": 1,
"maxLength": 255
},
"description": {
"type": "string",
"maxLength": 5000
},
"source_type": {
"type": "string",
"enum": [
"report",
"dashboard",
"goal",
"data_source"
],
"description": "Which clariBI artifact the metric lives on. Most callers should start with data_source for raw connector metrics or goal to forecast a tracked KPI."
},
"source_id": {
"type": "string",
"description": "UUID of the report, dashboard, goal, or data source."
},
"metric_path": {
"type": "string",
"description": "Dot-path to the numeric column. For data_source: the metric key (e.g. \"revenue\"). For goal: \"value\" or \"percentage\". For report: the path inside the insights JSON. For dashboard: \"widget_id:metric_name\"."
},
"granularity": {
"type": "string",
"enum": [
"hourly",
"daily",
"weekly",
"monthly",
"quarterly",
"annual"
],
"default": "daily",
"description": "Bucket size for the time series. Hourly requires an hourly data source; the engine refuses to fabricate hourly buckets from daily data."
},
"horizon_days": {
"type": "integer",
"minimum": 1,
"maximum": 168,
"default": 30,
"description": "How many BUCKETS ahead to project. The per-granularity cap applies: hourly 168, daily 90, weekly 52, monthly 24, quarterly 8, annual 5. The field name is \"_days\" for backwards compatibility — it counts buckets of the chosen granularity."
},
"include_correlations": {
"type": "boolean",
"default": true
},
"include_anomalies": {
"type": "boolean",
"default": true
},
"include_changepoints": {
"type": "boolean",
"default": true
},
"narration_enabled": {
"type": "boolean",
"default": true,
"description": "When true, every completed run gets an AI-generated narrative attached (summary, highlights, risks, recommendations, methodology). Costs 1-2 extra AI credits per run."
},
"aggregation": {
"type": "string",
"enum": [
"auto",
"sum",
"mean",
"last",
"max"
],
"default": "auto",
"description": "How multiple raw points falling into the same bucket are combined. Auto picks sum for additive metrics, mean otherwise. Use Mean for rates (CTR, conversion, latency)."
},
"transform": {
"type": "string",
"enum": [
"auto",
"none",
"log"
],
"default": "auto",
"description": "Series transform applied before fitting. Log helps revenue/traffic series with growing variance. Auto detects when log is beneficial; none forces raw scale."
},
"non_negative": {
"type": "boolean",
"description": "When true, point + lower band clamped at zero. Omit to let the engine heuristic decide (via non_negative_auto=true)."
},
"non_negative_auto": {
"type": "boolean",
"default": true,
"description": "When true (default), the backend heuristic owns non_negative and re-evaluates per run based on the metric path. Set false alongside non_negative to lock the choice."
},
"method_override": {
"type": "string",
"maxLength": 40,
"description": "Force a specific forecasting method instead of auto-selecting via walk-forward CV. Valid names: naive, seasonal_naive, moving_average, drift, linear_trend, ar_p, holt_winters, holt_winters_multiplicative, holt_winters_damped, theta, gradient_boost, ensemble_top3. Empty = auto."
},
"schedule_frequency": {
"type": "string",
"enum": [
"daily",
"weekly",
"monthly",
"manual"
],
"default": "monthly",
"description": "How often the forecast re-runs. Manual schedules only run when called explicitly via run_forecast."
},
"schedule_day_of_week": {
"type": "integer",
"minimum": 0,
"maximum": 6
},
"schedule_day_of_month": {
"type": "integer",
"minimum": 1,
"maximum": 31
}
},
"additionalProperties": false
}