Time Series Clustering With Random Convolutional Kernels

• URL: http://arxiv.org/abs/2305.10457v2
• Date: Thu, 6 Jul 2023 13:36:47 GMT
• Title: Time Series Clustering With Random Convolutional Kernels
• Authors: Jorge Marco-Blanco, Rub\'en Cuevas
• Abstract summary: Time series data, spanning applications ranging from climatology to finance to healthcare, presents significant challenges in data mining. One open issue lies in time series clustering, which is crucial for processing large volumes of unlabeled time series data. We introduce R-Clustering, a novel method that utilizes convolutional architectures with randomly selected parameters.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Time series data, spanning applications ranging from climatology to finance to healthcare, presents significant challenges in data mining due to its size and complexity. One open issue lies in time series clustering, which is crucial for processing large volumes of unlabeled time series data and unlocking valuable insights. Traditional and modern analysis methods, however, often struggle with these complexities. To address these limitations, we introduce R-Clustering, a novel method that utilizes convolutional architectures with randomly selected parameters. Through extensive evaluations, R-Clustering demonstrates superior performance over existing methods in terms of clustering accuracy, computational efficiency and scalability. Empirical results obtained using the UCR archive demonstrate the effectiveness of our approach across diverse time series datasets. The findings highlight the significance of R-Clustering in various domains and applications, contributing to the advancement of time series data mining.

Related papers

• Concrete Dense Network for Long-Sequence Time Series Clustering [4.307648859471193]
  Time series clustering is fundamental in data analysis for discovering temporal patterns. Deep temporal clustering methods have been trying to integrate the canonical k-means into end-to-end training of neural networks. LoSTer is a novel dense autoencoder architecture for the long-sequence time series clustering problem.
  arXiv  Detail & Related papers  (2024-05-08T12:31:35Z)
• Fuzzy clustering of circular time series based on a new dependence measure with applications to wind data [2.845817138242963]
  Time series clustering is an essential machine learning task with applications in many disciplines. A distance between circular series is introduced and used to construct a clustering procedure. A fuzzy approach is adopted, which enables the procedure to locate each series into several clusters with different membership degrees.
  arXiv  Detail & Related papers  (2024-01-26T12:21:57Z)
• Robust Detection of Lead-Lag Relationships in Lagged Multi-Factor Models [61.10851158749843]
  Key insights can be obtained by discovering lead-lag relationships inherent in the data. We develop a clustering-driven methodology for robust detection of lead-lag relationships in lagged multi-factor models.
  arXiv  Detail & Related papers  (2023-05-11T10:30:35Z)
• Grouped self-attention mechanism for a memory-efficient Transformer [64.0125322353281]
  Real-world tasks such as forecasting weather, electricity consumption, and stock market involve predicting data that vary over time. Time-series data are generally recorded over a long period of observation with long sequences owing to their periodic characteristics and long-range dependencies over time. We propose two novel modules, Grouped Self-Attention (GSA) and Compressed Cross-Attention (CCA) Our proposed model efficiently exhibited reduced computational complexity and performance comparable to or better than existing methods.
  arXiv  Detail & Related papers  (2022-10-02T06:58:49Z)
• Multi-scale Attention Flow for Probabilistic Time Series Forecasting [68.20798558048678]
  We propose a novel non-autoregressive deep learning model, called Multi-scale Attention Normalizing Flow(MANF) Our model avoids the influence of cumulative error and does not increase the time complexity. Our model achieves state-of-the-art performance on many popular multivariate datasets.
  arXiv  Detail & Related papers  (2022-05-16T07:53:42Z)
• Cluster-and-Conquer: A Framework For Time-Series Forecasting [94.63501563413725]
  We propose a three-stage framework for forecasting high-dimensional time-series data. Our framework is highly general, allowing for any time-series forecasting and clustering method to be used in each step. When instantiated with simple linear autoregressive models, we are able to achieve state-of-the-art results on several benchmark datasets.
  arXiv  Detail & Related papers  (2021-10-26T20:41:19Z)
• Novel Features for Time Series Analysis: A Complex Networks Approach [62.997667081978825]
  Time series data are ubiquitous in several domains as climate, economics and health care. Recent conceptual approach relies on time series mapping to complex networks. Network analysis can be used to characterize different types of time series.
  arXiv  Detail & Related papers  (2021-10-11T13:46:28Z)
• PIETS: Parallelised Irregularity Encoders for Forecasting with Heterogeneous Time-Series [5.911865723926626]
  Heterogeneity and irregularity of multi-source data sets present a significant challenge to time-series analysis. In this work, we design a novel architecture, PIETS, to model heterogeneous time-series. We show that PIETS is able to effectively model heterogeneous temporal data and outperforms other state-of-the-art approaches in the prediction task.
  arXiv  Detail & Related papers  (2021-09-30T20:01:19Z)
• Deep Cellular Recurrent Network for Efficient Analysis of Time-Series Data with Spatial Information [52.635997570873194]
  This work proposes a novel deep cellular recurrent neural network (DCRNN) architecture to process complex multi-dimensional time series data with spatial information. The proposed architecture achieves state-of-the-art performance while utilizing substantially less trainable parameters when compared to comparable methods in the literature.
  arXiv  Detail & Related papers  (2021-01-12T20:08:18Z)
• Hierarchical Clustering using Auto-encoded Compact Representation for Time-series Analysis [8.660029077292346]
  We propose a novel mechanism to identify the clusters combining learned compact representation of time-series, Auto Encoded Compact Sequence (AECS) and hierarchical clustering approach. Our algorithm exploits Recurrent Neural Network (RNN) based under complete Sequence to Sequence(seq2seq) autoencoder and agglomerative hierarchical clustering.
  arXiv  Detail & Related papers  (2021-01-11T08:03:57Z)
• Autoencoder-based time series clustering with energy applications [0.0]
  Time series clustering is a challenging task due to the specific nature of the data. In this paper we investigate the combination of a convolutional autoencoder and a k-medoids algorithm to perfom time series clustering.
  arXiv  Detail & Related papers  (2020-02-10T10:04:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.