DEEP LEARNING TECHNIQUES FOR TIME SERIES DATA MINING ANOMALY DETECTION

Abstract

As communications innovations develop and ventures become automated, a wide scope of applications and frameworks have arisen to provide and produce enormous measures of data. Numerous techniques have been proposed to separate key indicators from a lot of data to address the state of the whole framework. Inconsistencies utilizing such indicators are identified quickly to forestall likely accidents and limit economic misfortunes. Multivariate anomaly detection of time series data is especially difficult because it requires concurrent consideration of time, dependencies, and relationships between factors. Profound learning-based works have made tremendous advancements around here. Representations of huge scope successions in records may be prepared and advanced in an unaided way and identify oddities from the data. Be that as it may, the vast majority of them are unmistakable to an individual use case and in this manner require space information for legitimate arrangement. This paper provides a logical foundation for anomaly detection in time series data and surveys state-of-the-workmanship certifiable applications. We likewise investigate techniques appropriate for profound time series anomaly detection models utilizing a few standard datasets. At long last, we present a plan for choosing and preparing a fitting model, a profound learning-based time series anomaly detection procedure.