Self-learning systems and methods using moving euclidean distance machine-learning models for anomaly and trend detection in timeseries data

A method of analyzing datasets to detect anomalies in data values contained in the datasets, the method comprising:

• receiving, via a system controller from a data source, a timeseries dataset containing a sequence of data values recorded within a select time period;
• determining, via the system controller for the timeseries dataset, a window length defining a number of sequential values in the sequence of data values assigned to each of a series of data windows;
• enumerating, via the system controller from the timeseries dataset, the series of data windows including assigning a respective subsequence of values from the sequence of data values to each data window in the series of data windows;
• generating, via the system controller based on a total number of the data windows, a distance value matrix containing an array of matrix cells, each cell in the array of matrix cells including a Euclidean distance between a respective pair of the data windows;
• generating, via the system controller from the distance value matrix, a shortest distance (SD) matrix containing an array of SD values, each of the SD values defining a shortest distance between each of the data windows and every other one of the data windows;
• and
• analyzing, via the system controller, the SD array to determine whether or not one of the SD values is irregular relative to the other SD values and thereby indicates an anomaly.