Papers Time Series Clustering
“Time Series Clustering” 태그가 달린 논문 121편 · 필터 해제
Efficient Time Series Clustering from Multiscale Reservoir Dynamics with Granular-Ball Anchoring Graph Optimization
Time-series clustering remains challenging due to the inherent trade-off between clustering effectiveness and computational efficiency. Similarity-based methods often suffer from quadratic complexity caused by pairwise d…
Computational EfficiencyTime Series ClusteringAmortized Neural Clustering of Time Series based on Statistical Features
This paper introduces an algorithm-agnostic approach to feature-based time series clustering via amortized neural inference. By training neural networks to approximate the optimal partitioning rule from simulated data, t…
Time Series ClusteringScalable inference of spatial regions and temporal signatures from time series
Regionalization aims to partition a spatial domain into contiguous regions that share similar characteristics, enabling more effective spatial analysis, policy making, and resource management. Existing approaches for spa…
Time Series ClusteringLDTC: Lifelong deep temporal clustering for multivariate time series
Clustering temporal and dynamically changing multivariate time series from real-world fields, called temporal clustering for short, has been a major challenge due to inherent complexities. Although several deep temporal …
Dimensionality ReductionTime Series ClusteringA Quantum Tensor Network-Based Viewpoint for Modeling and Analysis of Time Series Data
Accurate uncertainty quantification is a critical challenge in machine learning. While neural networks are highly versatile and capable of learning complex patterns, they often lack interpretability due to their ``black …
Change Point DetectionTime Series ClusteringImproving Internet Traffic Matrix Prediction via Time Series Clustering
We present a novel framework that leverages time series clustering to improve internet traffic matrix (TM) prediction using deep learning (DL) models. Traffic flows within a TM often exhibit diverse temporal behaviors, w…
Time Series ClusteringTowards Explainable Deep Clustering for Time Series Data
Deep clustering uncovers hidden patterns and groups in complex time series data, yet its opaque decision-making limits use in safety-critical settings. This survey offers a structured overview of explainable deep cluster…
Time Series ClusteringDeep ClusteringVolatility Spillovers and Interconnectedness in OPEC Oil Markets: A Network-Based log-ARCH Approach
This paper examines several network-based volatility models for oil prices, capturing spillovers among OPEC oil-exporting countries by embedding novel network structures into ARCH-type models. We apply a network-based lo…
Time Series ClusteringGlobalization for Scalable Short-term Load Forecasting
Forecasting load in power transmission networks is essential across various hierarchical levels, from the system level down to individual points of delivery (PoD). While intuitive and locally accurate, traditional local …
Time Series ClusteringUnsupervised Clustering for Fault Analysis in High-Voltage Power Systems Using Voltage and Current Signals
The widespread use of sensors in modern power grids has led to the accumulation of large amounts of voltage and current waveform data, especially during fault events. However, the lack of labeled datasets poses a signifi…
Fault DiagnosisRTETime Series ClusteringA system identification approach to clustering vector autoregressive time series
Clustering of time series based on their underlying dynamics is keeping attracting researchers due to its impacts on assisting complex system modelling. Most current time series clustering methods handle only scalar time…
ClusteringTime SeriesTime Series ClusteringCSTS: A Benchmark for the Discovery of Correlation Structures in Time Series Clustering
Time series clustering promises to uncover hidden structural patterns in data with applications across healthcare, finance, industrial systems, and other critical domains. However, without validated ground truth informat…
ClusteringClustering Algorithms EvaluationClustering Multivariate Time SeriesTime Series+14TaStiC: Time and trend traveling time series clustering for classifying long-term type 2 diabetes patients
Diabetes is one of the most prevalent diseases worldwide, characterized by persistently high blood sugar levels, capable of damaging various internal organs and systems. Diabetes patients require routine check-ups, resul…
ClusteringTime SeriesTime Series ClusteringRanked differences Pearson correlation dissimilarity with an application to electricity users time series clustering
Time series clustering is an unsupervised learning method for classifying time series data into groups with similar behavior. It is used in applications such as healthcare, finance, economics, energy, and climate science…
ClusteringTime SeriesTime Series ClusteringPolyspectral Mean based Time Series Clustering of Indian Stock Market
In this study, we employ k-means clustering algorithm of polyspectral means to analyze 49 stocks in the Indian stock market. We have used spectral and bispectral information obtained from the data, by using spectral and …
ClusteringTime SeriesTime Series ClusteringExamining the Dynamics of Local and Transfer Passenger Share Patterns in Air Transportation
The air transportation local share, defined as the proportion of local passengers relative to total passengers, serves as a critical metric reflecting how economic growth, carrier strategies, and market forces jointly in…
ClusteringTime Series Clustering$k$-Graph: A Graph Embedding for Interpretable Time Series Clustering
Time series clustering poses a significant challenge with diverse applications across domains. A prominent drawback of existing solutions lies in their limited interpretability, often confined to presenting users with ce…
ClusteringGraph EmbeddingTime SeriesTime Series ClusteringA causal learning approach to in-orbit inertial parameter estimation for multi-payload deployers
This paper discusses an approach to inertial parameter estimation for the case of cargo carrying spacecraft that is based on causal learning, i.e. learning from the responses of the spacecraft, under actuation. Different…
parameter estimationTime SeriesTime Series ClusteringBridging the Gap: A Decade Review of Time-Series Clustering Methods
Time series, as one of the most fundamental representations of sequential data, has been extensively studied across diverse disciplines, including computer science, biology, geology, astronomy, and environmental sciences…
AstronomyClusteringSurveyTime Series+1TNNGen: Automated Design of Neuromorphic Sensory Processing Units for Time-Series Clustering
Temporal Neural Networks (TNNs), a special class of spiking neural networks, draw inspiration from the neocortex in utilizing spike-timings for information processing. Recent works proposed a microarchitecture framework …
Time SeriesTime Series Clustering