Missing data imputation for transfer passenger flow identified from in-station WiFi systems

Jiang, Wenhua; Zheng, Nan; Kim, Lee-Hyung

doi:10.1080/21680566.2022.2064935

Cited by 3 publications

(1 citation statement)

References 33 publications

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“…GP regression model is a nonparametric method and shows excellent fitting capability for various functions [24,25]. Recently, MGP has attracted increasing attention used for a multitask learning model with the ability to describe the correlation among different outputs [26], which has been widely employed in the capacity forecast of battery cells [27], traffic modeling [28], WiFi sensor system [29], and multienvironmental trial [30]. For instance, Rodrigues et al [28] proposed the Bayesian nonparametric formalism-based MGP to model the complex spatial and temporal relationships in crowdsourced traffic data.…”

Section: Introductionmentioning

confidence: 99%

Regularized Multioutput Gaussian Convolution Process for Chemical Contents Data Imputation in Sintering Raw Materials

Liu,

Chen,

et al. 2023

IET Signal Processing

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Chemical contents, the important quality indicators are crucial for the modeling of sintering process. However, the lack of these data can result in the biasedness of state estimation in sintering process. It, thus, greatly reduces the accuracy of modeling. Although there are some general imputation methods to tackle the data lackness, they rarely consider the interoutputs correlation and the negative impacts caused by incorrect prefilling. In this article, a novel sparse multioutput Gaussian convolution process (MGCP) modeling framework is proposed for data imputation. MGCP can flexibly mine the relationships between the outputs by a convolution of a sharing latent function and different Gaussian kernels. Moreover, the penalization terms are designed to weaken the false relationship between these outputs. To generalize the MGCP to a long-period case, dynamic time warping term is introduced to keep the global similarity between the original and estimated time series. Compared with several existing methods, the proposed method shows great superiority in sintering raw material contents estimation with real-world data.

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