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 …
Driver identification using automobile sensor data from a single turn
As automotive electronics continue to advance, cars are becoming more and more reliant on
sensors to perform everyday driving operations. These sensors are omnipresent and help …
sensors to perform everyday driving operations. These sensors are omnipresent and help …
Clustering and classification for time series data in visual analytics: A survey
Visual analytics for time series data has received a considerable amount of attention.
Different approaches have been developed to understand the characteristics of the data and …
Different approaches have been developed to understand the characteristics of the data and …
[HTML][HTML] Convolutional autoencoder-based ground motion clustering and selection
Ground motion selection has become increasingly central to the assessment of earthquake
resilience. The selection of ground motion records for use in nonlinear dynamic analysis …
resilience. The selection of ground motion records for use in nonlinear dynamic analysis …
Traffic prediction with transfer learning: A mutual information-based approach
In modern traffic management, one of the most essential yet challenging tasks is accurately
and timely predicting traffic. It has been well investigated and examined that deep learning …
and timely predicting traffic. It has been well investigated and examined that deep learning …
Multivariate time series clustering and forecasting for building energy analysis: Application to weather data quality control
L Sanhudo, J Rodrigues… - Journal of Building …, 2021 - Elsevier
In recent years, several tools for building energy analysis and simulation have been
developed to assist in increasing building energy performance, harvesting its computing …
developed to assist in increasing building energy performance, harvesting its computing …
[图书][B] Time series knowlegde mining.
F Mörchen - 2006 - academia.edu
An important goal of knowledge discovery is the search for patterns in data that can help
explain the underlying process that generated the data. The patterns are required to be new …
explain the underlying process that generated the data. The patterns are required to be new …
Analyzing the Dynamics of Customer Behavior: A New Perspective on Personalized Marketing through Counterfactual Analysis
M Ebadi Jalal, A Elmaghraby - Journal of Theoretical and Applied …, 2024 - mdpi.com
The existing body of research on dynamic customer segmentation has primarily focused on
segment-level customer purchasing behavior (CPB) analysis to tailor marketing strategies …
segment-level customer purchasing behavior (CPB) analysis to tailor marketing strategies …
pdc: An R package for complexity-based clustering of time series
AM Brandmaier - Journal of Statistical Software, 2015 - jstatsoft.org
Permutation distribution clustering is a complexity-based approach to clustering time series.
The dissimilarity of time series is formalized as the squared Hellinger distance between the …
The dissimilarity of time series is formalized as the squared Hellinger distance between the …
Machine learning approach for sequence clustering with applications to ground-motion selection
Clustering analysis of sequential data is of great interest and importance in many science
and engineering areas thanks to the explosive growth of time-series data. Effective methods …
and engineering areas thanks to the explosive growth of time-series data. Effective methods …