Main Reference PaperEffective Prediction of Missing Data on Apache Spark over Multivariable Time Series, IEEE Transactions on Big Data, 2018 [Python/Apache spark]
  • The matrix factorization technique is proposed for predicting the missing data in the time series from multiple sources, which satisfy the performance of missing data prediction but high computing efficiency. To reduce the computation, this work aim at fusing the smoothness characteristic of each time series and valuable correlation information across multiple sources into matrix factorization. To optimize the solution of matrix factorization, this work proposed a scheme are correlated sensors based regularization (CSR) term and the uncorrelated sensors based regularization (USR).

Description
  • The matrix factorization technique is proposed for predicting the missing data in the time series from multiple sources, which satisfy the performance of missing data prediction but high computing efficiency. To reduce the computation, this work aim at fusing the smoothness characteristic of each time series and valuable correlation information across multiple sources into matrix factorization. To optimize the solution of matrix factorization, this work proposed a scheme are correlated sensors based regularization (CSR) term and the uncorrelated sensors based regularization (USR).

  • To improve the accuracy of missing data prediction

  • To reduce the prediction error of the missing data prediction

Aim & Objectives
  • To improve the accuracy of missing data prediction

  • To reduce the prediction error of the missing data prediction

  • A technique is contributed to extend an algorithm in the proposed scheme.

Contribution
  • A technique is contributed to extend an algorithm in the proposed scheme.

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