Isaacburmingham/multivariate-time-series-anomaly-detection
Analyzing multiple multivariate time series datasets and using LSTMs and Nonparametric Dynamic Thresholding to detect anomalies across various industries.
This tool helps operations managers, data analysts, or engineers overseeing complex systems identify unusual patterns in their operational data. By analyzing multiple related data streams over time—like sensor readings, network traffic, or financial metrics—it flags points where system behavior deviates significantly from normal, providing early warnings of potential issues. It takes in historical time-series data from various sources and outputs specific timestamps and data points indicating anomalies.
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Use this if you need to automatically detect unexpected events or performance issues in systems that generate a lot of interconnected time-series data.
Not ideal if your data isn't time-series based, you only have a single data stream, or you need to predict future values rather than identify past anomalies.
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Last pushed
Jul 12, 2022
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