Approaches and applications of early classification of time series: A review
Early classification of time series has been extensively studied for minimizing class
prediction delay in time-sensitive applications such as medical diagnostic and industrial …
prediction delay in time-sensitive applications such as medical diagnostic and industrial …
Convolutional neural networks for time series classification
B Zhao, H Lu, S Chen, J Liu… - Journal of systems …, 2017 - ieeexplore.ieee.org
Time series classification is an important task in time series data mining, and has attracted
great interests and tremendous efforts during last decades. However, it remains a …
great interests and tremendous efforts during last decades. However, it remains a …
Intraday prediction of Borsa Istanbul using convolutional neural networks and feature correlations
Stock market price data have non-linear, noisy and non-stationary structure, and therefore
prediction of the price or its direction are both challenging tasks. In this paper, we propose a …
prediction of the price or its direction are both challenging tasks. In this paper, we propose a …
[HTML][HTML] Thirty years of credal networks: Specification, algorithms and complexity
Credal networks generalize Bayesian networks to allow for imprecision in probability values.
This paper reviews the main results on credal networks under strong independence, as …
This paper reviews the main results on credal networks under strong independence, as …
CNN approaches for time series classification
L Sadouk - Time series analysis-data, methods, and applications, 2019 - books.google.com
Time series classification is an important field in time series data-mining which have covered
broad applications so far. Although it has attracted great interests during last decades, it …
broad applications so far. Although it has attracted great interests during last decades, it …
Time-series clustering based on linear fuzzy information granules
In this paper, time-series clustering is discussed. At first ℓ 1 trend filtering method is used to
produce an optimal segmentation of time series. Next optimized fuzzy information …
produce an optimal segmentation of time series. Next optimized fuzzy information …
[HTML][HTML] Multivariate times series classification through an interpretable representation
Multivariate time series classification is a machine learning task with increasing importance
due to the proliferation of information sources in different domains (economy, health, energy …
due to the proliferation of information sources in different domains (economy, health, energy …
Unsupervised classification of multivariate time series using VPCA and fuzzy clustering with spatial weighted matrix distance
Due to high dimensionality and multiple variables, unsupervised classification of multivariate
time series (MTS) involves more challenging problems than those of univariate ones. Unlike …
time series (MTS) involves more challenging problems than those of univariate ones. Unlike …
Weak fault diagnosis of rotating machinery based on feature reduction with Supervised Orthogonal Local Fisher Discriminant Analysis
F Li, J Wang, MK Chyu, B Tang - Neurocomputing, 2015 - Elsevier
A new weak fault diagnosis method based on feature reduction with Supervised Orthogonal
Local Fisher Discriminant Analysis (SOLFDA) is proposed. In this method, the Shannon …
Local Fisher Discriminant Analysis (SOLFDA) is proposed. In this method, the Shannon …
CLR-based deep convolutional spiking neural network with validation based stop** for time series classification
A Gautam, V Singh - Applied Intelligence, 2020 - Springer
Huge amount of time series data over several domains such as engineering, biomedical and
finance, demands the development of efficient methods for the problem of time series …
finance, demands the development of efficient methods for the problem of time series …