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

TL;DR

This paper evaluates low overhead preprocessing techniques for multi-channel time series data to improve machine learning classification accuracy, addressing the alignment problem caused by data misalignment and demonstrating high accuracy results.

Contribution

It introduces three low overhead preprocessing methods for aligning multi-channel time series data, achieving over 95% classification accuracy and outperforming previous approaches.

Findings

Achieved over 95% classification accuracy.

Outperformed previous methods by 5%.

Proposed low overhead alignment techniques.

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.