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Implement ML Anomaly Detection

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Introduce statistical anomaly detection for metrics via edge function, hooks, and UI components. Adds detection algorithms (z-score, moving average, rate of change), anomaly storage, auto-alerts, and dashboard rendering of detected anomalies with run-once trigger and scheduling guidance.
This commit is contained in:
gpt-engineer-app[bot]
2025-11-11 02:07:49 +00:00
parent 7fba819fc7
commit be94b4252c
7 changed files with 887 additions and 0 deletions
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157
src/integrations/supabase/types.ts
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@@ -202,6 +202,111 @@ export type Database = {
}
Relationships: []
}
anomaly_detection_config: {
Row: {
alert_threshold_score: number
auto_create_alert: boolean
created_at: string
detection_algorithms: string[]
enabled: boolean
id: string
lookback_window_minutes: number
metric_category: string
metric_name: string
min_data_points: number
sensitivity: number
updated_at: string
}
Insert: {
alert_threshold_score?: number
auto_create_alert?: boolean
created_at?: string
detection_algorithms?: string[]
enabled?: boolean
id?: string
lookback_window_minutes?: number
metric_category: string
metric_name: string
min_data_points?: number
sensitivity?: number
updated_at?: string
}
Update: {
alert_threshold_score?: number
auto_create_alert?: boolean
created_at?: string
detection_algorithms?: string[]
enabled?: boolean
id?: string
lookback_window_minutes?: number
metric_category?: string
metric_name?: string
min_data_points?: number
sensitivity?: number
updated_at?: string
}
Relationships: []
}
anomaly_detections: {
Row: {
alert_created: boolean
alert_id: string | null
anomaly_type: string
anomaly_value: number
baseline_value: number
confidence_score: number
created_at: string
detected_at: string
detection_algorithm: string
deviation_score: number
id: string
metadata: Json | null
metric_category: string
metric_name: string
severity: string
time_window_end: string
time_window_start: string
}
Insert: {
alert_created?: boolean
alert_id?: string | null
anomaly_type: string
anomaly_value: number
baseline_value: number
confidence_score: number
created_at?: string
detected_at?: string
detection_algorithm: string
deviation_score: number
id?: string
metadata?: Json | null
metric_category: string
metric_name: string
severity: string
time_window_end: string
time_window_start: string
}
Update: {
alert_created?: boolean
alert_id?: string | null
anomaly_type?: string
anomaly_value?: number
baseline_value?: number
confidence_score?: number
created_at?: string
detected_at?: string
detection_algorithm?: string
deviation_score?: number
id?: string
metadata?: Json | null
metric_category?: string
metric_name?: string
severity?: string
time_window_end?: string
time_window_start?: string
}
Relationships: []
}
approval_transaction_metrics: {
Row: {
created_at: string | null
@@ -1894,6 +1999,36 @@ export type Database = {
}
Relationships: []
}
metric_time_series: {
Row: {
created_at: string
id: string
metadata: Json | null
metric_category: string
metric_name: string
metric_value: number
timestamp: string
}
Insert: {
created_at?: string
id?: string
metadata?: Json | null
metric_category: string
metric_name: string
metric_value: number
timestamp?: string
}
Update: {
created_at?: string
id?: string
metadata?: Json | null
metric_category?: string
metric_name?: string
metric_value?: number
timestamp?: string
}
Relationships: []
}
moderation_audit_log: {
Row: {
action: string
@@ -6270,6 +6405,28 @@ export type Database = {
}
Relationships: []
}
recent_anomalies_view: {
Row: {
alert_created: boolean | null
alert_id: string | null
alert_message: string | null
alert_resolved_at: string | null
anomaly_type: string | null
anomaly_value: number | null
baseline_value: number | null
confidence_score: number | null
detected_at: string | null
detection_algorithm: string | null
deviation_score: number | null
id: string | null
metric_category: string | null
metric_name: string | null
severity: string | null
time_window_end: string | null
time_window_start: string | null
}
Relationships: []
}
}
Functions: {
anonymize_user_submissions: {