1 research outputs found
Clustering of Time Series Data with Prior Geographical Information
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