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Calculating the matrix profile from noisy data.

Colin Hehir1, Alan F Smeaton1,2

  • 1School of Computing, Dublin City University, Glasnevin, Dublin, Ireland.

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This summary is machine-generated.

The matrix profile (MP) is resilient to minor noise in time series data. However, significant increases in noise disrupt the MP

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Area of Science:

  • Data Mining
  • Time Series Analysis
  • Pattern Recognition

Background:

  • The matrix profile (MP) is crucial for identifying patterns and outliers in time series data.
  • Traditional noise reduction methods are unsuitable for unsupervised learning scenarios.
  • The robustness of MP generation against noisy data is not well understood.

Purpose of the Study:

  • To investigate the resilience of matrix profile (MP) generation to noisy time series data.
  • To quantify the impact of varying noise levels on MP accuracy.

Main Methods:

  • Generated MPs from original time series data and data with added noise (duplicates, irrelevant data).
  • Compared MPs using similarity metrics across diverse, real-world datasets.
  • Evaluated MP performance under a range of noise parameter settings.

Main Results:

  • MP generation demonstrates resilience to small amounts of noise in time series.
  • As noise levels increase, the resilience of MP generation significantly diminishes.
  • Dissimilarities between MPs indicate a threshold beyond which noise impacts results.

Conclusions:

  • Matrix profile computation is robust to minor data perturbations.
  • Significant noise levels compromise the integrity and reliability of the matrix profile.
  • Further research is needed to develop noise-robust MP algorithms for complex, real-world applications.