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Abstract

The present disclosure is related to the field of threshold generation where complex temporal and spatial behavior of metrics are modelled to define normal operating baseline of every component in a system environment. Embodiments of the present disclosure provide methods and systems for generation and optimization of metric threshold for anomaly detection. In the present disclosure, temporal properties such as variation, behavioral patterns of a plurality of metrics are derived. The derived temporal properties are used to generate data-driven and domain-aware static or dynamic thresholds. Additionally, the present disclosure factors spatial and temporal properties collectively to mine a role of influencing metrics and define composite thresholds. In order to cater to dynamic behavior of the system environment and changes in business and technological aspects, the system and method of the present disclosure self-learns, self-tunes, and adapts itself based on user feedback which helps in capturing tacit knowledge of domain experts.

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