Abstract: Systems and methods for identifying suspected anomalies in time series data are disclosed. An example method may receiving time series data for at least one quantity, the time series data including values of the at least one quantity at each of a plurality of times, determining a list of gradients for the time series data, each gradient in the list of gradients based on two or more values of the time series data separated by a specified number of values of the time series data, deriving a plurality of statistics based on the determined list of gradients, and performing a supervised machine learning process based on the derived plurality of statistics to generate a trained machine learning model for identifying one or more suspected anomalies in the time series data.

Abstract: A computer-implemented method and system are provided to perform a machine learning pipeline process to produce an explainable machine learning model. A computing device may be configured to train a plurality of machine learning models with a set of respective feature datasets to generate an accuracy and explainability property for each trained model. The computing device may evaluate a plurality of the trained machine learning models and select a model as an explainable machine learning model based on at least one of the accuracy and the explainability property.