Abstract: Systems and methods for automated detection of website errors during a sequence of device interactions with a website. In one example, a computing device is configured to identify clickstream data associated with a client device interacting with a website and predict a clickstream metric for a subsequent interaction with the website based at least in part on the clickstream data. An anomaly website event for an interaction of the client device with the website is determined based on a measurement of the clickstream metric failing to reach a predefined range of the predicted clickstream metric. Anomaly type for the anomaly website event is determined based at least in part on a machine learning model being trained with a plurality of previous anomaly website events identified from a plurality of previous instances of clickstream data.

Abstract: Systems and methods for automated detection of website errors during a sequence of device interactions with a website. In one example, a computing device is configured to receive a website navigation sequence of a series of web page interactions between a client device and a website. The computing device can determine a predicted completion time for a next measurement of the client device executing the website navigation sequence. An actual completion time for the next measurement of the client device executing the website navigation sequence can be determined. Then, the computing device can determine an anomaly website event based on the actual competition time failing to meet a boundary threshold. The anomaly website event is determined to be a website error based on a second machine learning model being trained with a plurality of previous website errors identified from a plurality of previous website navigation sequences.

Abstract: Systems and methods for automated detection of website errors during a sequence of device interactions with a website. In one example, a computing device is configured to receive a website navigation sequence of a series of web page interactions between a client device and a website. The computing device can determine a predicted completion time for a next measurement of the client device executing the website navigation sequence. An actual completion time for the next measurement of the client device executing the website navigation sequence can be determined. Then, the computing device can determine an anomaly website event based on the actual competition time failing to meet a boundary threshold. The anomaly website event is determined to be a website error based on a second machine learning model being trained with a plurality of previous website errors identified from a plurality of previous website navigation sequences.