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Missing Data Imputation Method Combining Random Forest and Generative Adversarial Imputation Network.

Hongsen Ou1, Yunan Yao1, Yi He1

  • 1School of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology, Wuhan 430063, China.

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Summary
This summary is machine-generated.

This study introduces a novel method for filling missing time-series data using random forest and generative adversarial networks. The combined approach significantly improves data interpolation accuracy, outperforming existing methods.

Keywords:
data interpolationgenerative adversarial interpolation networkrandom foresttime-series data

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Area of Science:

  • Data Science
  • Machine Learning
  • Signal Processing

Background:

  • Missing time-series data poses challenges in various fields due to acquisition system failures or external interferences.
  • Accurate data imputation is crucial for reliable analysis and decision-making in time-series applications.

Purpose of the Study:

  • To develop an advanced data interpolation method for effectively addressing missing time-series data.
  • To combine the strengths of random forest and generative adversarial interpolation networks for enhanced imputation accuracy.

Main Methods:

  • A two-stage interpolation process is employed, starting with random forest for initial data filling.
  • The output from the random forest is then refined using a generative adversarial interpolation network for improved calibration and imputation.
  • This hybrid approach leverages the advantages of both algorithms to achieve results closer to the true data values.

Main Results:

  • The proposed method was evaluated on a bearing dataset, demonstrating strong performance in interpolating both single-segment and multi-segment missing data.
  • The root mean square error (RMSE) for the combined approach was found to be as low as 0.0157, 0.0386, and 0.0527.
  • Performance surpassed that of individual random forest, generative adversarial interpolation network, and K-nearest neighbor algorithms.

Conclusions:

  • The proposed random forest and generative adversarial interpolation network hybrid algorithm effectively handles missing time-series data.
  • This method offers a valuable reference for data imputation techniques in various scientific and engineering domains.