Time Series Clustering
30 papers with code • 1 benchmarks • 3 datasets
Time Series Clustering is an unsupervised data mining technique for organizing data points into groups based on their similarity. The objective is to maximize data similarity within clusters and minimize it across clusters. Time-series clustering is often used as a subroutine of other more complex algorithms and is employed as a standard tool in data science for anomaly detection, character recognition, pattern discovery, visualization of time series.
Source: Comprehensive Process Drift Detection with Visual Analytics
Libraries
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Latest papers with no code
Towards Financially Inclusive Credit Products Through Financial Time Series Clustering
Financial inclusion ensures that individuals have access to financial products and services that meet their needs.
Evaluation of k-means time series clustering based on z-normalization and NP-Free
By systematically investigating the performance of k-means time series clustering with these two normalization techniques, this paper addresses the current gap in k-means time series clustering evaluation and contributes valuable insights to the development of time series clustering.
Fuzzy clustering of circular time series based on a new dependence measure with applications to wind data
Time series clustering is an essential machine learning task with applications in many disciplines.
Discovery of Generalizable TBI Phenotypes Using Multivariate Time-Series Clustering
Importantly, while certain features varied by age, the core characteristics of TBI manifestations tied to each phenotype remain consistent across diverse populations.
Interpretable Spectral Variational AutoEncoder (ISVAE) for time series clustering
The best encoding is the one that is interpretable in nature.
Determining the Optimal Number of Clusters for Time Series Datasets with Symbolic Pattern Forest
The most widely used clustering algorithms like k-means and k-shape in time series data mining also need the ground truth for the number of clusters that need to be generated.
Diffeomorphic Transformations for Time Series Analysis: An Efficient Approach to Nonlinear Warping
Elastic metrics such as dynamic time warping (DTW) offer a promising approach, but are limited by their computational complexity, non-differentiability and sensitivity to noise and outliers.
Clustering of Urban Traffic Patterns by K-Means and Dynamic Time Warping: Case Study
The speed time series extracts the traffic pattern in different road segments.
Identifying Subgroups of ICU Patients Using End-to-End Multivariate Time-Series Clustering Algorithm Based on Real-World Vital Signs Data
This study employed the MIMIC-IV database as data source to investigate the use of dynamic, high-frequency, multivariate time-series vital signs data, including temperature, heart rate, mean blood pressure, respiratory rate, and SpO2, monitored first 8 hours data in the ICU stay.
Graph-based Time Series Clustering for End-to-End Hierarchical Forecasting
Existing relationships among time series can be exploited as inductive biases in learning effective forecasting models.