SYSTEMS AND METHODS FOR ANOMALY DETECTION AND GUIDED ANALYSIS USING STRUCTURAL TIME-SERIES MODELS

摘要

Systems and methods for anomaly detection and guided analysis using structural time-series model. A server may receive a request from a client to analyze a time-series data comprising a plurality of data points. A database of global calendars may be accessed. A structural time-series model may be built from the time-series data and the database of global calendars, the structural time-series model comprising a hidden structure and a plurality of probability distributions, each probability distribution corresponding to a data point. For each data point of the time-series data, a range of expected values is determined from a respective probability distribution, the range of expected values capturing a predefined percentage of the respective probability distribution. An anomaly is detected at a first data point of the time-series data responsive to comparing the first data point with a respective range of expected values. The anomaly is transmitted to the client for display with the time-series data.

主权项

1. A computer-implemented method for anomaly detection and forecasting time-series data, the method comprising:
receiving, at a server, a request from a client to analyze a time-series data comprising a plurality of data points; accessing a database of global calendars; building a structural time-series model from the time-series data and the database of global calendars, the structural time-series model comprising a hidden structure and a plurality of probability distributions, each probability distribution corresponding to a data point; determining, for each data point of the time-series data, a range of expected values from a respective probability distribution, the range of expected values capturing a predefined percentage of the respective probability distribution; detecting an anomaly at a first data point of the time-series data responsive to comparing the first data point with a respective range of expected values; and transmitting the anomaly to the client for display with the time-series data.