VMware, Inc.
NEURAL-NETWORK-BASED METHODS AND SYSTEMS THAT GENERATE ANOMALY SIGNALS FROM FORECASTS OF TIME-SERIES DATA

Abstract:

The current document is directed to improved system monitoring and management tools and methods based on generation an anomaly signal from time-series data collected from components of a computer system, providing improved system monitoring and management. The time series data comprises a time-ordered sequence of metric datapoints that is received over a period of time. At each of a set of discrete, successive time points within the period of time, a datapoint for the anomaly signal is generated from a forecast generated from a preceding set of time-series datapoints, referred to as a "history window," and a short segment of the time series, referred to as the "observation window," extending forward in time from the most recently datapoint in the history window. The anomaly signal predicts incipient anomalous conditions in the computer system.