Fuzzy clustering of ordinal time series based on two novel distances
with economic applications
- URL: http://arxiv.org/abs/2304.12249v1
- Date: Mon, 24 Apr 2023 16:39:22 GMT
- Title: Fuzzy clustering of ordinal time series based on two novel distances
with economic applications
- Authors: \'Angel L\'opez Oriona, Christian Weiss and Jos\'e Antonio Vilar
- Abstract summary: Two novel distances between ordinal time series are introduced and used to construct fuzzy clustering procedures.
The resulting clustering algorithms are computationally efficient and able to group series generated from similar processes.
Two specific applications involving economic time series illustrate the usefulness of the proposed approaches.
- Score: 0.12891210250935145
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Time series clustering is a central machine learning task with applications
in many fields. While the majority of the methods focus on real-valued time
series, very few works consider series with discrete response. In this paper,
the problem of clustering ordinal time series is addressed. To this aim, two
novel distances between ordinal time series are introduced and used to
construct fuzzy clustering procedures. Both metrics are functions of the
estimated cumulative probabilities, thus automatically taking advantage of the
ordering inherent to the series' range. The resulting clustering algorithms are
computationally efficient and able to group series generated from similar
stochastic processes, reaching accurate results even though the series come
from a wide variety of models. Since the dynamic of the series may vary over
the time, we adopt a fuzzy approach, thus enabling the procedures to locate
each series into several clusters with different membership degrees. An
extensive simulation study shows that the proposed methods outperform several
alternative procedures. Weighted versions of the clustering algorithms are also
presented and their advantages with respect to the original methods are
discussed. Two specific applications involving economic time series illustrate
the usefulness of the proposed approaches.
Related papers
- A Bayesian Mixture Model of Temporal Point Processes with Determinantal Point Process Prior [21.23523473330637]
Asynchronous event sequence clustering aims to group similar event sequences in an unsupervised manner.
Our work provides a flexible learning framework for event sequence clustering, enabling automatic identification of the potential number of clusters.
It is applicable to a wide range of parametric temporal point processes, including neural network-based models.
arXiv Detail & Related papers (2024-11-07T03:21:30Z) - 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) - An Efficient Algorithm for Clustered Multi-Task Compressive Sensing [60.70532293880842]
Clustered multi-task compressive sensing is a hierarchical model that solves multiple compressive sensing tasks.
The existing inference algorithm for this model is computationally expensive and does not scale well in high dimensions.
We propose a new algorithm that substantially accelerates model inference by avoiding the need to explicitly compute these covariance matrices.
arXiv Detail & Related papers (2023-09-30T15:57:14Z) - 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) - Gradient Based Clustering [72.15857783681658]
We propose a general approach for distance based clustering, using the gradient of the cost function that measures clustering quality.
The approach is an iterative two step procedure (alternating between cluster assignment and cluster center updates) and is applicable to a wide range of functions.
arXiv Detail & Related papers (2022-02-01T19:31:15Z) - 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) - Quantile-based fuzzy C-means clustering of multivariate time series:
Robust techniques [2.3226893628361682]
Robustness to the presence of outliers is achieved by using the so-called metric, noise and trimmed approaches.
Results from a broad simulation study indicate that the algorithms are substantially effective in coping with the presence of outlying series.
arXiv Detail & Related papers (2021-09-22T20:26:12Z) - Quantile-based fuzzy clustering of multivariate time series in the
frequency domain [2.610470075814367]
fuzzy C-means and fuzzy C-medoids algorithms are proposed.
The performance of the proposed approach is evaluated in a broad simulation study.
Two specific applications involving air quality and financial databases illustrate the usefulness of our approach.
arXiv Detail & Related papers (2021-09-08T15:38:33Z) - Conjoined Dirichlet Process [63.89763375457853]
We develop a novel, non-parametric probabilistic biclustering method based on Dirichlet processes to identify biclusters with strong co-occurrence in both rows and columns.
We apply our method to two different applications, text mining and gene expression analysis, and demonstrate that our method improves bicluster extraction in many settings compared to existing approaches.
arXiv Detail & Related papers (2020-02-08T19:41:23Z)
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.