Updating attribute data structures to indicate trends in attribute data provided to automated modelling systems

A method comprising:

• accessing a data structure having training data for an automated modeling algorithm, the training data comprising data items with attribute values for multiple entities over a time period, the training data mapping a respective attribute value for each of a plurality of attributes to a respective time value within the time period, and an entity identifier for a respective entity associated with the respective attribute value;
• generating, with a processing device, at least one trend attribute that is a function of a time series of attribute values, the at least one trend attribute indicating a trend in the time series of attribute values, wherein generating the at least one trend attribute comprises:
• applying, for each entity, a frequency transform to a respective subset of attribute values for the at least one attribute based on the respective subset of attribute values being associated with the entity, and
• selecting, as the at least one trend attribute, at least one coefficient generated by the applied frequency transform;
• modifying, with the processing device, the data structure to include the at least one trend attribute, wherein modifying the data structure includes storing entries mapping the respective entity identifiers to a respective plurality of trend attribute values;
• updating, with the processing device, the training data to include trend attribute values for the at least one trend attribute, the trend attribute values including values of the at least one coefficient, wherein updating the training data comprises performing, for at least some of the entities in the training data, operations comprising:
• identifying, for each entity, a respective cluster series having a respective behavioral attribute value that is similar to a respective behavior of a respective time series of attributes values for the entity,
• assigning a cluster membership to the entity based on the respective behavioral attribute value being similar to the respective behavior of the respective time series of attributes values for the entity, and
• selecting, for the entity, an identifier of the cluster membership as a trend attribute value for the entity;
• training, with the processing device, the automated modeling algorithm with the trend attribute values and one or more attribute values from the data structure, including providing the training data to the automated modeling algorithm to train the automated modeling algorithm to teach the automated modeling algorithm to predict a risk outcome;
• and
• outputting, with the processing device, the trend attribute values from the data structure to a computing system that executes the automated modeling algorithm.

A non-transitory computer-readable medium having program code that is executable by a processing device to cause a computing device to perform operations, the operations comprising:

• accessing a data structure comprising training data for training an automated modeling algorithm, the training data comprising data items with attribute values for multiple entities over a time period, the training data mapping a respective attribute value for each of a plurality of attributes to a respective time value within the time period, and an entity identifier for a respective entity associated with the respective attribute value;
• generating at least one trend attribute that is a function of a time series of attribute values, the at least one trend attribute indicating a trend in the time series of attribute values, wherein generating the at least one trend attribute comprises:
• (a) generating multiple cluster series, wherein each cluster series comprises clusters respectively associated with intervals in the time period, and
• (b) computing, for the multiple cluster series, respective behavioral attribute values, wherein each behavioral attribute value is computed as a function of the respective cluster series;
• modifying the data structure to include the at least one trend attribute, wherein modifying the data structure includes storing entries mapping the respective entity identifiers to a respective plurality of trend attribute values;
• updating the training data to include trend attribute values for the at least one trend attribute, wherein updating the training data comprises performing, for at least some of the entities in the training data comprises:
• identifying, for each entity, a respective cluster series having a respective behavioral attribute value that is similar to a respective behavior of a respective time series of attributes values for the entity,
• assigning a cluster membership to the entity based on the respective behavioral attribute value being similar to the respective behavior of the respective time series of attributes values for the entity, and
• selecting, for the entity, an identifier of the cluster membership as a trend attribute value for the entity;
• and
• training the automated modeling algorithm with the trend attribute values and one or more attribute values from the data structure, including providing the training data to the automated modeling algorithm to train the automated modeling algorithm to teach the automated modeling algorithm to predict a risk outcome.