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A novel mixed frequency sampling discrete grey model for forecasting hard disk drive failure.

Rongxing Chen1, Xinping Xiao1, Mingyun Gao2

  • 1School of Science, Wuhan University of Technology, Wuhan 430070, China.

ISA Transactions
|March 7, 2024
PubMed
Summary

A new mixed frequency sampling discrete grey model (MDGM(1, N)) enhances prediction for mixed frequency data, outperforming existing models in small sample environments. This novel approach improves hard disk drive failure forecasting accuracy and stability.

Keywords:
Chimp optimization algorithmHard disk drive failure forecastingMixed data samplingMixed frequency sampling grey model

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Area of Science:

  • Data Science
  • Time Series Analysis
  • Reliability Engineering

Background:

  • Mixed data sampling (MIDAS) models excel with mixed frequency data but struggle in small samples.
  • Existing MIDAS extensions often lack prediction performance and stability.

Purpose of the Study:

  • Introduce a novel mixed frequency sampling discrete grey model (MDGM(1, N)).
  • Enhance prediction accuracy and stability for mixed frequency data, especially in small sample scenarios.
  • Improve hard disk drive failure forecasting.

Main Methods:

  • Coupled MIDAS and discrete grey multivariate models (MDGM(1, N)).
  • Mathematical analysis and random experiments for unbiasedness and stability.
  • Meta-heuristic algorithms for optimal parameter selection.
  • Model evaluation system with traditional metrics and monotonicity tests.

Main Results:

  • MDGM(1, N) demonstrated superior validity, stability, and robustness compared to seven benchmark models.
  • Uncorrectable errors and command timeouts significantly impact hard disk drive failures.
  • Accurate forecasting of hard disk drive failures for four specific models.

Conclusions:

  • The proposed MDGM(1, N) is effective for mixed frequency data analysis and forecasting.
  • The model offers improved performance in small sample environments.
  • The study provides valuable insights into hard disk drive failure prediction.