Suggestions on how to detect anomalies in around 200k time series, maybe with deep learning, maybe in a fast way?
2 visualizaciones (últimos 30 días)
Mostrar comentarios más antiguos
I have tried Time Series Anomaly Detection Using Deep Learning, with just a few thousands of time series, but it is very slow...
Any suggestion to get anomalies detected with 200k time series in a faster way?
0 comentarios
Respuesta aceptada
Eamonn
el 18 de Jun. de 2022
Hello
The DAMP algorithm (in Matlab) can do anomlay detecion at 100,000+ Hz
It is the only time series anomlay detecion algorthm to process a dataset with one trillion datapoints.
OH, and it beat deep learning on all the benchmarks.
It will be here https://www.cs.ucr.edu/~eamonn/MatrixProfile.html next week, but in the meantime, if you want the code/paper, I am happy to share it.
6 comentarios
Más respuestas (1)
Image Analyst
el 18 de Jun. de 2022
Are you talking about training or prediction?
For what it's worth, here is another anomaly detection demo from Mathworks.
Learn how to apply statistical and machine learning based anomaly detection techniques to industrial processes and machinery.
3 comentarios
Image Analyst
el 19 de Jun. de 2022
Well of course you could always get a faster computer with more memory. Get the best video adapter you can afford with the most GPU memory you can afford. And get the Parallel Processing Toolbox, which will speed it up quite a bit (if you end up having to use the CPU because your GPU does not have enough memory and you get an "out of memory" error). The GPU does not know how to use disk space to allocate additional memory (everything happens on the GPU) but the CPU can get as much virtual memory from the disk drive as it needs (or that you allow).
Ver también
Categorías
Más información sobre Parallel and Cloud en Help Center y File Exchange.
Productos
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!