EnsiyeTahaei/DeepAnT-Time-Series-Anomaly-Detection
An implementation of the DeepAnT model, a deep learning approach for unsupervised anomaly detection in time series data, using Python.
This tool helps you automatically find unusual patterns or unexpected spikes in your business or operational data that changes over time, like sensor readings, sales figures, or network traffic. It takes a stream of continuous data (a time series) and identifies points or periods that deviate significantly from the norm, flagging them as anomalies. Operations managers, data analysts, and researchers who monitor trends and need to detect unusual events would find this useful.
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Use this if you have continuous, time-ordered data and need to automatically identify unexpected events or outliers without manually setting rules.
Not ideal if your data is static (not a time series) or if you already have clear labels for what constitutes an anomaly.
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11
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Language
Python
License
MIT
Category
Last pushed
Feb 15, 2025
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