Continuous Conditional Random Fields in Predicting High-Dimensional Data

The increase of vehicle in highways may cause traffic congestion as well as in the normal roadways. Predicting the traffic flow in highways especially, is demanded to solve this congestion problem. Predictions on time-series multivariate data, such as in the traffic flow dataset, have been largely accomplished through various approaches. The approach with conventional prediction algorithms, such as with Support Vector Machine (SVM), is only capable of accommodating predictions that are independent in each time unit. Hence, the sequential relationships in this time series data is hardly explored. Continuous Conditional Random Field (CCRF) is one of Probabilistic Graphical Model (PGM) algorithms which can accommodate this problem. The neighboring aspects of sequential data such as in the time series data can be expressed by CCRF so that its predictions are more reliable. In this article, CCRF is implemented to increase the prediction ability of different baseline regressors, i.e. SVM and Extreme Learning Machine (ELM). Both algorithms are examined in two different datasets. The result shows that CCRF is superior in performance when examined using high-dimensional dataset. This is validated by the increasing of the coefficient of correlation of the baseline up to 7.3% of significance.