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dbt_project.yml
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dbt_project.yml
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name: 'anomaly_detection'
version: '1.0.0'
require-dbt-version: '>=1.0.0'
config-version: 2
vars:
anomaly_detection_aggregation_levels: [4, 8, 12, 24]
anomaly_detection_prob_thresholds : [0.9999, 0.999999]
anomaly_detection_horizon: 120
anomaly_detection_forecast_interval: 10
anomaly_detection_holiday_region: "CA"
data_interval: 90
start_date: "\"2023-02-09\""
# start_date: "CURRENT_DATE()"
IQR_coeff: 4.5
cutoff_count: 50
recent_event_cutoff: 30
neg_lower_bound_reset: 2
bounds_coeff: 1.3
# sources vars
source_table: sample_table_final
source_name: sampled_data
# input fields
collector_tstamp: collector_tstamp #collector timestamps
event_id: event_id #unique identifiers
app_event: app_event #aggregation field
# level of granularity and training interval for ML models
models:
1mon_8hr:
period: "8hr"
train_interval: 60
2mon_24hr:
period: "24hr"
train_interval: 90
2mon_12hr:
period: "12hr"
train_interval: 90
2mon_8hr:
period: "8hr"
train_interval: 90
2mon_4hr:
period: "4hr"
train_interval: 90
1mon_4hr:
period: "4hr"
train_interval: 60
1mon_12hr:
period: "12hr"
train_interval: 60
1mon_24hr:
period: "24hr"
train_interval: 60
05mon_4hr:
period: "4hr"
train_interval: 30
05mon_8hr:
period: "8hr"
train_interval: 30
05mon_12hr:
period: "12hr"
train_interval: 30
05mon_24hr:
period: "24hr"
train_interval: 30
models:
anomaly_detection:
+materialized: view
anomaly_detection:
forecasts:
+pre-hook:
- "{{ create_models() }}"