Dynamic clustering of time series data
- URL: http://arxiv.org/abs/2002.01890v1
- Date: Tue, 28 Jan 2020 12:01:28 GMT
- Title: Dynamic clustering of time series data
- Authors: Victhor S. Sart\'orio and Tha\'is C. O. Fonseca
- Abstract summary: We propose a new method for clustering multivariate time-series data based on Dynamic Linear Models.
In this context, a mixture model is assumed for the time series and a flexible Dirichlet evolution for mixture weights allows for smooth membership changes over time.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We propose a new method for clustering multivariate time-series data based on
Dynamic Linear Models. Whereas usual time-series clustering methods obtain
static membership parameters, our proposal allows each time-series to
dynamically change their cluster memberships over time. In this context, a
mixture model is assumed for the time series and a flexible Dirichlet evolution
for mixture weights allows for smooth membership changes over time. Posterior
estimates and predictions can be obtained through Gibbs sampling, but a more
efficient method for obtaining point estimates is presented, based on
Stochastic Expectation-Maximization and Gradient Descent. Finally, two
applications illustrate the usefulness of our proposed model to model both
univariate and multivariate time-series: World Bank indicators for the
renewable energy consumption of EU nations and the famous Gapminder dataset
containing life-expectancy and GDP per capita for various countries.
Related papers
- Time Series Clustering with General State Space Models via Stochastic Variational Inference [0.0]
We propose a novel method of model-based time series clustering with mixtures of general state space models (MSSMs)
An advantage of the proposed method is that it enables the use of time series models appropriate to the specific time series.
Experiments on simulated datasets show that the proposed method is effective for clustering, parameter estimation, and estimating the number of clusters.
arXiv Detail & Related papers (2024-06-29T12:48:53Z) - PDETime: Rethinking Long-Term Multivariate Time Series Forecasting from
the perspective of partial differential equations [49.80959046861793]
We present PDETime, a novel LMTF model inspired by the principles of Neural PDE solvers.
Our experimentation across seven diversetemporal real-world LMTF datasets reveals that PDETime adapts effectively to the intrinsic nature of the data.
arXiv Detail & Related papers (2024-02-25T17:39:44Z) - Unified Training of Universal Time Series Forecasting Transformers [104.56318980466742]
We present a Masked-based Universal Time Series Forecasting Transformer (Moirai)
Moirai is trained on our newly introduced Large-scale Open Time Series Archive (LOTSA) featuring over 27B observations across nine domains.
Moirai achieves competitive or superior performance as a zero-shot forecaster when compared to full-shot models.
arXiv Detail & Related papers (2024-02-04T20:00:45Z) - TACTiS-2: Better, Faster, Simpler Attentional Copulas for Multivariate Time Series [57.4208255711412]
Building on copula theory, we propose a simplified objective for the recently-introduced transformer-based attentional copulas (TACTiS)
We show that the resulting model has significantly better training dynamics and achieves state-of-the-art performance across diverse real-world forecasting tasks.
arXiv Detail & Related papers (2023-10-02T16:45:19Z) - Parameterization of state duration in Hidden semi-Markov Models: an
application in electrocardiography [0.0]
We introduce a parametric model for time series pattern recognition and provide a maximum-likelihood estimation of its parameters.
An application on classification reveals the main strengths and weaknesses of each alternative.
arXiv Detail & Related papers (2022-11-17T11:51:35Z) - Time Series Clustering with an EM algorithm for Mixtures of Linear
Gaussian State Space Models [0.0]
We propose a novel model-based time series clustering method with mixtures of linear Gaussian state space models.
The proposed method uses a new expectation-maximization algorithm for the mixture model to estimate the model parameters.
Experiments on a simulated dataset demonstrate the effectiveness of the method in clustering, parameter estimation, and model selection.
arXiv Detail & Related papers (2022-08-25T07:41:23Z) - 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) - Instance-wise Graph-based Framework for Multivariate Time Series
Forecasting [69.38716332931986]
We propose a simple yet efficient instance-wise graph-based framework to utilize the inter-dependencies of different variables at different time stamps.
The key idea of our framework is aggregating information from the historical time series of different variables to the current time series that we need to forecast.
arXiv Detail & Related papers (2021-09-14T07:38:35Z) - Improving the Accuracy of Global Forecasting Models using Time Series
Data Augmentation [7.38079566297881]
Forecasting models that are trained across sets of many time series, known as Global Forecasting Models (GFM), have shown promising results in forecasting competitions and real-world applications.
We propose a novel, data augmentation based forecasting framework that is capable of improving the baseline accuracy of GFM models in less data-abundant settings.
arXiv Detail & Related papers (2020-08-06T13:52:20Z)
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.