Causal discovery from temporal data: An overview and new perspectives
Temporal data, representing chronological observations of complex systems, has always
been a typical data structure that can be widely generated by many domains, such as …
been a typical data structure that can be widely generated by many domains, such as …
Deep learning approaches for similarity computation: A survey
The requirement for appropriate ways to measure the similarity between data objects is a
common but vital task in various domains, such as data mining, machine learning and so on …
common but vital task in various domains, such as data mining, machine learning and so on …
GRLSTM: trajectory similarity computation with graph-based residual LSTM
The computation of trajectory similarity is a crucial task in many spatial data analysis
applications. However, existing methods have been designed primarily for trajectories in …
applications. However, existing methods have been designed primarily for trajectories in …
An anomaly detection framework for time series data: An interval-based approach
Y Zhou, H Ren, Z Li, W Pedrycz - Knowledge-Based Systems, 2021 - Elsevier
Due to the high data volume and non-stationarity of time series data, it is very difficult to
directly use the original data for anomaly detection. In this study, a novel framework of …
directly use the original data for anomaly detection. In this study, a novel framework of …
Mutual distillation learning network for trajectory-user linking
Trajectory-User Linking (TUL), which links trajectories to users who generate them, has
been a challenging problem due to the sparsity in check-in mobility data. Existing methods …
been a challenging problem due to the sparsity in check-in mobility data. Existing methods …
Long-term multivariate time series forecasting model based on Gaussian fuzzy information granules
C Zhu, X Ma, P D'Urso, Y Qian… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Long-term forecasting of multivariate time series has been an important research issue in
the field of data mining and knowledge discovery. Fuzzy information granularity is used as …
the field of data mining and knowledge discovery. Fuzzy information granularity is used as …
Belief Re´ nyi Divergence of Divergence and Its Application in Time Series Classification
L Zhang, F **ao - IEEE Transactions on Knowledge and Data …, 2024 - ieeexplore.ieee.org
Time series data contains the amount of information to reflect the development process and
state of a subject. Especially, the complexity is a valuable factor to illustrate the feature of the …
state of a subject. Especially, the complexity is a valuable factor to illustrate the feature of the …
Selecting the optimal gridded climate dataset for Nigeria using advanced time series similarity algorithms
Choosing a suitable gridded climate dataset is a significant challenge in hydro-climatic
research, particularly in areas lacking long-term, reliable, and dense records. This study …
research, particularly in areas lacking long-term, reliable, and dense records. This study …
Deep dirichlet process mixture model for non-parametric trajectory clustering
Trajectory clustering is an essential task in spatial data mining. To address this problem,
many previous studies either extended traditional clustering algorithms with spatial features …
many previous studies either extended traditional clustering algorithms with spatial features …
Spatial-temporal fusion graph framework for trajectory similarity computation
Trajectory similarity computation is an essential operation in many applications of spatial
data analysis. In this paper, we study the problem of trajectory similarity computation over …
data analysis. In this paper, we study the problem of trajectory similarity computation over …