An Evaluation of Low Overhead Time Series Preprocessing Techniques for Downstream Machine Learning

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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 classification with the MIT SuperCloud Dataset by 5 %. These results indicate our low overhead approaches to solving the alignment problem, in conjunction with standard machine learning techniques, are able to achieve high levels of classification accuracy, and serve as a baseline for future approaches to addressing the alignment problem, such as kernel methods.

Original languageEnglish
Title of host publication2022 IEEE High Performance Extreme Computing Conference, HPEC 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665497862
DOIs
StatePublished - 2022
Externally publishedYes
Event2022 IEEE High Performance Extreme Computing Conference, HPEC 2022 - Virtual, Online, United States
Duration: Sep 19 2022Sep 23 2022

Publication series

Name2022 IEEE High Performance Extreme Computing Conference, HPEC 2022

Conference

Conference2022 IEEE High Performance Extreme Computing Conference, HPEC 2022
Country/TerritoryUnited States
CityVirtual, Online
Period09/19/2209/23/22

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