spencerbraun/anomaly_transformer_pytorch
PyTorch implementation of Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy
This helps operations engineers and data analysts detect unusual patterns or outliers in their time-series data. It takes in historical time-series datasets, like server logs or sensor readings, and identifies points in time that deviate significantly from expected behavior. The output highlights these anomalies, helping users proactively address issues or investigate unexpected events.
252 stars. No commits in the last 6 months.
Use this if you need to automatically spot unusual spikes, dips, or other irregular activities in continuous data streams from sensors, IT systems, or financial markets.
Not ideal if your data is not time-series based, or if you need to predict future values rather than just detect current anomalies.
Stars
252
Forks
56
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Jan 24, 2023
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/transformers/spencerbraun/anomaly_transformer_pytorch"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
amazon-science/chronos-forecasting
Chronos: Pretrained Models for Time Series Forecasting
SalesforceAIResearch/uni2ts
Unified Training of Universal Time Series Forecasting Transformers
moment-timeseries-foundation-model/moment
MOMENT: A Family of Open Time-series Foundation Models, ICML'24
ServiceNow/TACTiS
TACTiS-2: Better, Faster, Simpler Attentional Copulas for Multivariate Time Series, from...
yotambraun/APDTFlow
APDTFlow is a modern and extensible forecasting framework for time series data that leverages...