Deep time-series clustering: A review
We present a comprehensive, detailed review of time-series data analysis, with emphasis on
deep time-series clustering (DTSC), and a case study in the context of movement behavior …
deep time-series clustering (DTSC), and a case study in the context of movement behavior …
Graph attention network with spatial-temporal clustering for traffic flow forecasting in intelligent transportation system
With the development of the Internet of Things (IoT) and 5G technologies, IoT devices
deployed on roads are able to collect a large amount of traffic data at any time. Road …
deployed on roads are able to collect a large amount of traffic data at any time. Road …
Spatio-temporal data clustering using deep learning: A review
R Aparna, SM Idicula - 2022 IEEE International Conference on …, 2022 - ieeexplore.ieee.org
Spatial and temporal information are recorded with each measurement in so-called
spatiotemporal (ST) data. The space-time information added to each measurement makes …
spatiotemporal (ST) data. The space-time information added to each measurement makes …
Extracting Spatiotemporal Bus Passenger Trip Typologies from Noisy Mobile Ticketing Boarding Data
M Abdalazeem, J Oke - Data Science for Transportation, 2023 - Springer
We present a framework for extracting spatiotemporal trip typologies using noisy mobile
ticketing boarding data sampled from passengers in a bus network. Our case study was the …
ticketing boarding data sampled from passengers in a bus network. Our case study was the …
Discovering Causes of Traffic Congestion via Deep Transfer Clustering
Traffic congestion incurs long delay in travel time, which seriously affects our daily travel
experiences. Exploring why traffic congestion occurs is significantly important to effectively …
experiences. Exploring why traffic congestion occurs is significantly important to effectively …
An Adaptive Ensemble Learning Paradigm With Spatial-Temporal Feature Extraction for Wireless Traffic Prediction
Accurately predicting traffic in a cellular network is challenging since the traffic time series
integrated by various wireless services is non-stationary and reveals concealed spatial …
integrated by various wireless services is non-stationary and reveals concealed spatial …
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 …
data area challenging example of spatio-temporal data, as it is multi-variate time series with …
A novel asymmetric loss function for deep clustering-based health monitoring and anomaly detection for spacecraft telemetry
Aerospace systems essentially require health monitoring and anomaly detection to enhance
system safety and reliability and to avoid system failure in spacecraft and satellites operating …
system safety and reliability and to avoid system failure in spacecraft and satellites operating …
Learning to Discover Causes of Traffic Congestion with Limited Labeled Data
Traffic congestion incurs long delay in travel time, which seriously affects our daily travel
experiences. Exploring why traffic congestion occurs is significantly important to effectively …
experiences. Exploring why traffic congestion occurs is significantly important to effectively …
1DCAE-TSSAMC: Two-Stage Multi-Dimensional Spatial Features Based Multi-View Deep Clustering for Time Series Data
J Chen, W Song, X Zuo, K Zhao, B Jin… - International Journal of …, 2024 - World Scientific
At present, as a research hotspot for time series data (TSD), the deep clustering analysis of
TSD has huge research value and practical significance. However, there still exist the …
TSD has huge research value and practical significance. However, there still exist the …