Statistical analysis with missing data
Statistical analysis with missing data
Numerical methods and software
Numerical methods and software
A unifying review of linear Gaussian models
Neural Computation
Proceedings of the 1998 conference on Advances in neural information processing systems II
Principal Component Analysis with Missing Data and Its Application to Polyhedral Object Modeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
High breakdown estimators for principal components: the projection-pursuit approach revisited
Journal of Multivariate Analysis
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Resampling Methods: A Practical Guide to Data Analysis
Resampling Methods: A Practical Guide to Data Analysis
On Bayesian principal component analysis
Computational Statistics & Data Analysis
Principal component analysis for data containing outliers and missing elements
Computational Statistics & Data Analysis
Spatial-temporal traffic data analysis based on global data management using MAS
IEEE Transactions on Intelligent Transportation Systems
A bayesian network approach to traffic flow forecasting
IEEE Transactions on Intelligent Transportation Systems
Real-time road traffic forecasting using regime-switching space-time models and adaptive LASSO
Applied Stochastic Models in Business and Industry
| Hi-index | 0.00 |
The missing data problem greatly affects traffic analysis. In this paper, we put forward a new reliable method called probabilistic principal component analysis (PPCA) to impute the missing flow volume data based on historical data mining. First, we review the current missing data-imputation method and why it may fail to yield acceptable results in many traffic flow applications. Second, we examine the statistical properties of traffic flow volume time series. We show that the fluctuations of traffic flow are Gaussian type and that principal component analysis (PCA) can be used to retrieve the features of traffic flow. Third, we discuss how to use a robust PCA to filter out the abnormal traffic flow data that disturb the imputation process. Finally, we recall the theories of PPCA/Bayesian PCA-based imputation algorithms and compare their performance with some conventional methods, including the nearest/mean historical imputation methods and the local interpolation/regression methods. The experiments prove that the PPCA method provides significantly better performance than the conventional methods, reducing the root-mean-square imputation error by at least 25%.