Clustering of Time Series Data with Prior Geographical Information

TL;DR

This paper introduces Spatial-DEC, a deep clustering model that leverages prior geographical information to improve spatio-temporal clustering of traffic flow data, enhancing analysis and machine learning applications.

Contribution

It presents a novel variation of Deep Embedded Clustering that incorporates geographical priors for better spatio-temporal clustering of traffic data.

Findings

Clusters have higher temporal similarity based on DTW distance.

Clusters better reflect spatial connectivity and dis-connectivity.

Spatial-DEC outperforms existing methods in identifying meaningful clusters.

Abstract

Time Series data are broadly studied in various domains of transportation systems. Traffic data area challenging example of spatio-temporal data, as it is multi-variate time series with high correlations in spatial and temporal neighborhoods. Spatio-temporal clustering of traffic flow data find similar patterns in both spatial and temporal domain, where it provides better capability for analyzing a transportation network, and improving related machine learning models, such as traffic flow prediction and anomaly detection. In this paper, we propose a spatio-temporal clustering model, where it clusters time series data based on spatial and temporal contexts. We propose a variation of a Deep Embedded Clustering(DEC) model for finding spatio-temporal clusters. The proposed model Spatial-DEC (S-DEC) use prior geographical information in building latent feature representations. We also define evaluation metrics for spatio-temporal clusters. Not only do the obtained clusters have better temporal similarity when evaluated using DTW distance, but also the clusters better represents spatial connectivity and dis-connectivity. We use traffic flow data obtained by PeMS in our analysis. The results show that the proposed Spatial-DEC can find more desired spatio-temporal clusters.