Detecting and validating anomalies from ongoing data collection

A system comprising:

• at least one processor;
• and
• at least one non-transitory computer-readable storage medium storing instructions that, when executed by the at least one processor, cause the system to:
• utilize a time series classification model to determine a time series classification corresponding to an ongoing data collection;
• select, based on the time series classification corresponding to the ongoing data collection, a data pattern for the ongoing data collection;
• determine a custom-fit algorithm for monitoring the ongoing data collection based on the data pattern for the ongoing data collection by automatically applying a tuning function to modify one or more parameters of the custom-fit algorithm relative to the data pattern for the ongoing data collection, wherein determining the custom-fit algorithm comprises generating kernel parameters from a Gaussian process regression by maximizing a log marginal likelihood utilizing a data-fit term, a complexity penalty, and a normalization constant within the Gaussian process regression;
• monitor the ongoing data collection utilizing the custom-fit algorithm corresponding to the data pattern;
• utilize the custom-fit algorithm corresponding to the data pattern to identify an anomaly candidate from the ongoing data collection;
• validate the anomaly candidate as an anomaly by passing the anomaly candidate through an anomaly filter;
• and
• in response to validating the anomaly candidate, provide an anomaly notification comprising information about the anomaly via a data report graphical user interface.

A non-transitory computer-readable medium storing instructions that, when executed by at least one processor, cause a computer device to:

• utilize a time series classification model to determine a time series classification corresponding to an ongoing data collection;
• select, based on the time series classification corresponding to the ongoing data collection, a data pattern for the ongoing data collection;
• determine a custom-fit algorithm for monitoring the ongoing data collection based on the data pattern for the ongoing data collection by automatically applying a tuning function to modify one or more parameters of the custom-fit algorithm relative to the data pattern for the ongoing data collection, wherein determining the custom-fit algorithm further comprises generating kernel parameters from a Gaussian process regression by maximizing a log marginal likelihood utilizing a data-fit term, a complexity penalty, and a normalization constant within the Gaussian process regression;
• monitor the ongoing data collection utilizing the custom-fit algorithm corresponding to the data pattern;
• utilize the custom-fit algorithm corresponding to the data pattern to identify an anomaly candidate from the ongoing data collection;
• validate the anomaly candidate as an anomaly by passing the anomaly candidate through an anomaly filter;
• and
• in response to validating the anomaly candidate, provide an anomaly notification comprising information about the anomaly via a data report graphical user interface.