Abstract: Systems and methods of anomaly detection using an optimal reference value are disclosed. A plurality of source-specific anchor values are received and a plurality of model features are generated. A plurality of trained source-specific classification models, each associated with at least one of the plurality of source-specific anchor values and each configured to receive a subset of the plurality of model features, are implemented. Each of the plurality of trained source-specific classification models is configured to classify the associated at least one of the plurality of source-specific anchor values as one of anomalous or non-anomalous. A trained weighted classification model is implemented to generate an optimal anchor value. The optimal anchor value includes a weighted aggregation of each of the plurality of source-specific anchor values identified as non-anomalous. An optimal reference value is generated based on the optimal anchor value.

Abstract: Systems and methods including one or more processors and one or more non-transitory computer readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform: receiving historical marketplace information corresponding to one or more categories, wherein the historical marketplace information is for a merchant in a marketplace; determining a respective anchor for each of the one or more categories of the historical marketplace information; receiving an observed price for an item in the marketplace, wherein the observed price corresponds to an item in the one or more categories; normalizing the anchors for each of the one or more categories based on the observed price and the historical marketplace information; determining if the observed price is an anomaly based on the anchors, as normalized; and transmitting an alert to the merchant in the marketplace when an anomaly is detected. Other embodiments are disclosed.