Temporal Dynamic Matrix Factorization for Missing Data Prediction in Large Scale Coevolving Time Series

Shi, Weiwei; Zhu, Yongxin; Yu, Philip S.; Huang, Tian; Wang, Chang; Mao, Yishu; Chen, Yufeng

doi:10.1109/access.2016.2606242

Cited by 19 publications

(7 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The least square SVM [16] technique is being proposed which helps in making the complex problem to linear regression one. Then by applying genetic algorithm over this LS-SVM [17], optimal parameters [18] [19] are obtained. The proposed system is compared with other existing systems like artificial neural network and it is found that the LS SVM based system perform far better than that.…”

Section: Related Workmentioning

confidence: 99%

Missing Data Prediction using Correlation Genetic Algorithm and SVM Approach

Alhroob¹,

Alzyadat²,

Almukahel³

et al. 2020

IJACSA

View full text Add to dashboard Cite

Data exists in large volume in the modern world, it becomes very useful when decoded correctly to inform decision making towards tackling real word issues. However, when the data is conflicting, it becomes a daunting task to get obtain information. Working on missing data has become a very important task in big data analysis. This paper considers the handling of the missing data using the Support Vector Machine (SVM) based on a technique called Correlation-Genetic Algorithm-SVM. This data is to be subjected to the SVM classification technique after identifying the attribute's correlation and application of the genetic algorithm. The application of the correlation enables a clear view of the attributes which are highly correlated within a particular dataset. The results indicate that apart from the SVM, the application of the proposed hybrid algorithm produces better outcomes identification rate and accuracy is considered. The proposed approach is also compared with depicts the Mean Identification rate of applying the neural network, the result indicate a consistent accuracy hence making it better.

show abstract

Section: Related Workmentioning

confidence: 99%

Missing Data Prediction using Correlation Genetic Algorithm and SVM Approach

Alhroob¹,

Alzyadat²,

Almukahel³

et al. 2020

IJACSA

View full text Add to dashboard Cite

show abstract

“…A considerable amount of literature has been published on time series with missing values. Many of these works focus on the imputation of missing values [15,25]. The classification problem can be solved after the imputation procedure using traditional classification methods such as kernel method [26], support vector machines [27] and random forest [12].…”

Section: Time Series With the Missing Values Classification Problemmentioning

confidence: 99%

“…Most people impute missing values with the mean value in the training set (mean imputation) or the last observation (forward imputation) for effectiveness and efficiency [14]. We can apply not only the simple methods mentioned above but also various advanced methods, such as matrix factorization [15], kernel methods [16], and the EM algorithm [17], to perform the imputation. However, missing data imputation only serves as an auxiliary function to improve classification accuracy, and some advanced methods may cause time-consuming and expensive computational problems without classification performance improvement.…”

Section: Introductionmentioning

confidence: 99%

VS-GRU: A Variable Sensitive Gated Recurrent Neural Network for Multivariate Time Series with Massive Missing Values

2019

Applied Sciences

View full text Add to dashboard Cite

Multivariate time series are often accompanied with missing values, especially in clinical time series, which usually contain more than 80% of missing data, and the missing rates between different variables vary widely. However, few studies address these missing rate differences and extract univariate missing patterns simultaneously before mixing them in the model training procedure. In this paper, we propose a novel recurrent neural network called variable sensitive GRU (VS-GRU), which utilizes the different missing rate of each variable as another input and learns the feature of different variables separately, reducing the harmful impact of variables with high missing rates. Experiments show that VS-GRU outperforms the state-of-the-art method in two real-world clinical datasets (MIMIC-III, PhysioNet).

show abstract

“…Traditional methods such as zero imputation or mean imputation ease the analysis but may lead to low imputation accuracy. For the datasets with missing values, matrix factorization based methods are shown to be effective for many missing value imputation applications (Shi et al, 2016;Troyanskaya et al, 2001), and frequently used for other applications of the matrix completion problem, i.e., collaborative filtering (Ocepek et al, 2015). Many efficient algorithms have been proposed, such as Singular Value Thresholding (SVT) (Cai et al, 2010), Fixed Point Continuation (FPC) (Ma et al, 2011), and Inexact Augmented Lagrange Multiplier (IALM) (Lin et al, 2010).…”

Section: Related Workmentioning

confidence: 99%

Simultaneous Measurement Imputation and Outcome Prediction for Achilles Tendon Rupture Rehabilitation

Hamesse,

Tu,

Ackermann

et al. 2018

Preprint

View full text Add to dashboard Cite

Achilles Tendon Rupture (ATR) is one of the typical soft tissue injuries. Rehabilitation after such a musculoskeletal injury remains a prolonged process with a very variable outcome. Accurately predicting rehabilitation outcome is crucial for treatment decision support. However, it is challenging to train an automatic method for predicting the ATR rehabilitation outcome from treatment data, due to a massive amount of missing entries in the data recorded from ATR patients, as well as complex nonlinear relations between measurements and outcomes. In this work, we design an end-to-end probabilistic framework to impute missing data entries and predict rehabilitation outcomes simultaneously. We evaluate our model on a real-life ATR clinical cohort, comparing with various baselines. The proposed method demonstrates its clear superiority over traditional methods which typically perform imputation and prediction in two separate stages.

show abstract

Temporal Dynamic Matrix Factorization for Missing Data Prediction in Large Scale Coevolving Time Series

Cited by 19 publications

References 20 publications

Missing Data Prediction using Correlation Genetic Algorithm and SVM Approach

Missing Data Prediction using Correlation Genetic Algorithm and SVM Approach

VS-GRU: A Variable Sensitive Gated Recurrent Neural Network for Multivariate Time Series with Massive Missing Values

Simultaneous Measurement Imputation and Outcome Prediction for Achilles Tendon Rupture Rehabilitation

Contact Info

Product

Resources

About