Capturing Value with Anomaly Detection - Concord White Paper

anomaly detection methods

Statistical Time Series (e.g., ARIMA, SARIMAX)

Clustering Methods (e.g., DBSCAN)

Statistical time series models are grounded in proven,

Clustering techniques identify patterns by grouping

scientific methods and are often used to forecast trends

data based on similarity. These methods can effectively

and detect deviations. These models are relatively easy

flag outliers by defining what’s "normal." While they offer

to implement and interpret but may struggle with

valuable insights, costs can increase with data volume

irregular processes or multivariate relationships.

and complexity.

Type:

Unsupervised / Supervised

Unsupervised

Complexity:

Medium

Medium – High

typical use cases:

Identifying out-of-plan behavior, spotting counter-

Detecting unusual data combinations, identifying

seasonal trends, uncovering marketing opportunities,

suspicious behavior, finding unique selling points,

measuring trend-based metrics over time

understanding natural groupings in data

Capturing Value with Anomaly Detection