Related papers: Multivariable times series classification through an interpretable representation

Multivariable times series classification through an interpretable representation

URL: http://arxiv.org/abs/2009.03614v1
Date: Tue, 8 Sep 2020 09:44:03 GMT
Title: Multivariable times series classification through an interpretable representation
Authors: Francisco J. Bald\'an, Jos\'e M. Ben\'itez
Abstract summary: We propose a time series classification method that considers an alternative representation of time series through a set of descriptive features. We have applied traditional classification algorithms obtaining interpretable and competitive results.
Score: 0.0
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Multivariate time series classification is a task with increasing importance due to the proliferation of new problems in various fields (economy, health, energy, transport, crops, etc.) where a large number of information sources are available. Direct extrapolation of methods that traditionally worked in univariate environments cannot frequently be applied to obtain the best results in multivariate problems. This is mainly due to the inability of these methods to capture the relationships between the different variables that conform a multivariate time series. The multivariate proposals published to date offer competitive results but are hard to interpret. In this paper we propose a time series classification method that considers an alternative representation of time series through a set of descriptive features taking into account the relationships between the different variables of a multivariate time series. We have applied traditional classification algorithms obtaining interpretable and competitive results.

Related papers

DRFormer: Multi-Scale Transformer Utilizing Diverse Receptive Fields for Long Time-Series Forecasting [3.420673126033772]
We propose a dynamic tokenizer with a dynamic sparse learning algorithm to capture diverse receptive fields and sparse patterns of time series data. Our proposed model, named DRFormer, is evaluated on various real-world datasets, and experimental results demonstrate its superiority compared to existing methods.
arXiv Detail & Related papers (2024-08-05T07:26:47Z)
MGCP: A Multi-Grained Correlation based Prediction Network for Multivariate Time Series [54.91026286579748]
We propose a Multi-Grained Correlations-based Prediction Network. It simultaneously considers correlations at three levels to enhance prediction performance. It employs adversarial training with an attention mechanism-based predictor and conditional discriminator to optimize prediction results at coarse-grained level.
arXiv Detail & Related papers (2024-05-30T03:32:44Z)
Compatible Transformer for Irregularly Sampled Multivariate Time Series [75.79309862085303]
We propose a transformer-based encoder to achieve comprehensive temporal-interaction feature learning for each individual sample. We conduct extensive experiments on 3 real-world datasets and validate that the proposed CoFormer significantly and consistently outperforms existing methods.
arXiv Detail & Related papers (2023-10-17T06:29:09Z)
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)
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)
Machine Learning for Multi-Output Regression: When should a holistic multivariate approach be preferred over separate univariate ones? [62.997667081978825]
Tree-based ensembles such as the Random Forest are modern classics among statistical learning methods. We compare these methods in extensive simulations to help in answering the primary question when to use multivariate ensemble techniques.
arXiv Detail & Related papers (2022-01-14T08:44:25Z)
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)
Explainable Multivariate Time Series Classification: A Deep Neural Network Which Learns To Attend To Important Variables As Well As Informative Time Intervals [32.30627405832656]
Time series data is prevalent in a wide variety of real-world applications. Key criterion to understand such predictive models involves elucidating and quantifying the contribution of time varying input variables to the classification. We introduce a novel, modular, convolution-based feature extraction and attention mechanism that simultaneously identifies the variables as well as time intervals which determine the classification output.
arXiv Detail & Related papers (2020-11-23T19:16:46Z)
Complexity Measures and Features for Times Series classification [0.0]
We propose a set of characteristics capable of extracting information on the structure of the time series to face time series classification problems. The experimental results of our proposal show no statistically significant differences from the second and third best models of the state-of-the-art.
arXiv Detail & Related papers (2020-02-27T11:08:08Z)

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