Can I read An Evaluation of Low Overhead Time Series Preprocessing Techniques for Downstream Machine Learning on EtoBox?

An Evaluation of Low Overhead Time Series Preprocessing Techniques for Downstream Machine Learning by Weiss, Matthew L.; McDonald, Joseph; Bestor, David; Yee, Charles; Edelman, Daniel; Jones, Michael; Prout, Andrew; Bowne, Andrew; McEvoy, Lindsey; Gadepally, Vijay; Samsi, Siddharth is a scholarly article available to read on EtoBox.

What is An Evaluation of Low Overhead Time Series Preprocessing Techniques for Downstream Machine Learning about?

In this paper we address the application of pre-processing techniques to multi-channel time series data with varying lengths, which we refer to as the alignment problem, for downstream machine learning. The misalignment of multi-channel time series data may occur for a variety of reasons, such as missing data, varying sampling rates, or inconsistent collection times. We consider multi-channel time series data collected from the MIT SuperCloud High Performance Computing (HPC) center, where different job start times and varying run times of HPC jobs result in misaligned data. This misalignment makes it challenging to build AI/ML approaches for tasks such as compute workload classification. Building on previous supervised classification work with the MIT SuperCloud Dataset, we address the alignment problem via three broad, low overhead approaches: sampling a fixed subset from a full time series, performing summary statistics on a full time series, and sampling a subset of coefficients from time series mapped to the frequency domain. Our best performing models achieve a classification accuracy greater than 95%, outperforming previous approaches to multi-channel time series classificati

Author

Weiss, Matthew L.; McDonald, Joseph; Bestor, David; Yee, Charles; Edelman, Daniel; Jones, Michael; Prout, Andrew; Bowne, Andrew; McEvoy, Lindsey; Gadepally, Vijay; Samsi, Siddharth

Published

2022

Language

EN