Misalignment problem in matrix decomposition with missing values

Fernandes, Sofia; Antunes, Mário; Gomes, Diogo; Aguiar, Rui L.

doi:10.1007/s10994-021-05985-w

Cited by 4 publications

(1 citation statement)

References 23 publications

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“…Severe missing values can result in inaccurate forecast values. A similar phenomenon was reported by Fernandes et al [ 43 ], which they refer to as the “misalignment problem in matrix factorization with missing values”. This problem is illustrated in Figure 5 : even though the nonnegative matrix factorization captures accurately the evolution of the time series in the missing gaps, the estimated range of values is far from the actual range of values.…”

Section: Proposed Methodssupporting

confidence: 78%

Robust Multi-Dimensional Time Series Forecasting

Shen,

He,

Qin

2024

Entropy

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Large-scale and high-dimensional time series data are widely generated in modern applications such as intelligent transportation and environmental monitoring. However, such data contains much noise, outliers, and missing values due to interference during measurement or transmission. Directly forecasting such types of data (i.e., anomalous data) can be extremely challenging. The traditional method to deal with anomalies is to cut out the time series with anomalous value entries or replace the data. Both methods may lose important knowledge from the original data. In this paper, we propose a multidimensional time series forecasting framework that can better handle anomalous values: the robust temporal nonnegative matrix factorization forecasting model (RTNMFFM) for multi-dimensional time series. RTNMFFM integrates the autoregressive regularizer into nonnegative matrix factorization (NMF) with the application of the L2,1 norm in NMF. This approach improves robustness and alleviates overfitting compared to standard methods. In addition, to improve the accuracy of model forecasts on severely missing data, we propose a periodic smoothing penalty that keeps the sparse time slices as close as possible to the time slice with high confidence. Finally, we train the model using the alternating gradient descent algorithm. Numerous experiments demonstrate that RTNMFFM provides better robustness and better prediction accuracy.

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Section: Proposed Methodssupporting

confidence: 78%

Robust Multi-Dimensional Time Series Forecasting

Shen,

He,

Qin

2024

Entropy

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Partially multi-view clustering via re-alignment

Yan,

Zhu,

Chen

et al. 2025

Neural Networks

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ImputeVIS: An Interactive Evaluator to Benchmark Imputation Techniques for Time Series Data

Khayati,

Nater,

Pasquier

2024

Proc. VLDB Endow.

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With the emergence of The Internet of Things (IoT), smart sensors have become abundant in our daily lives. Failures are very common in those devices, leaving the recorded time series with missing blocks of consecutive values. A cottage industry of imputation algorithms exists, each with different performance tradeoffs. The diversity in time series features, missingness patterns, and algorithms' categories makes it challenging to select the best algorithm. In this demonstration, we showcase ImputeVIS, an analytical tool for benchmarking imputation algorithms. ImputeVIS provides an optimal configuration of those algorithms by implementing various AutoML parameterization strategies. Moreover, it uncovers the behavior of imputation algorithms by explaining the interplay between time series features and the imputation results. Its interactive web browser interface allows users to simulate real-world sensor malfunctions by contaminating time series with different missing block scenarios, deploy their imputation algorithms, and compare them against various popular imputation families.

show abstract

Misalignment problem in matrix decomposition with missing values

Cited by 4 publications

References 23 publications

Robust Multi-Dimensional Time Series Forecasting

Robust Multi-Dimensional Time Series Forecasting

Partially multi-view clustering via re-alignment

ImputeVIS: An Interactive Evaluator to Benchmark Imputation Techniques for Time Series Data

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