11 research outputs found
Classifying motion states of AUV based on graph representation for multivariate time series
Acknowledgement This work is supported by Natural Science Foundation of Shandong Province (ZR2020MF079) and China Scholarship Council (CSC).Peer reviewedPostprin
Reservoir computing approaches for representation and classification of multivariate time series
Classification of multivariate time series (MTS) has been tackled with a
large variety of methodologies and applied to a wide range of scenarios.
Reservoir Computing (RC) provides efficient tools to generate a vectorial,
fixed-size representation of the MTS that can be further processed by standard
classifiers. Despite their unrivaled training speed, MTS classifiers based on a
standard RC architecture fail to achieve the same accuracy of fully trainable
neural networks. In this paper we introduce the reservoir model space, an
unsupervised approach based on RC to learn vectorial representations of MTS.
Each MTS is encoded within the parameters of a linear model trained to predict
a low-dimensional embedding of the reservoir dynamics. Compared to other RC
methods, our model space yields better representations and attains comparable
computational performance, thanks to an intermediate dimensionality reduction
procedure. As a second contribution we propose a modular RC framework for MTS
classification, with an associated open-source Python library. The framework
provides different modules to seamlessly implement advanced RC architectures.
The architectures are compared to other MTS classifiers, including deep
learning models and time series kernels. Results obtained on benchmark and
real-world MTS datasets show that RC classifiers are dramatically faster and,
when implemented using our proposed representation, also achieve superior
classification accuracy
Deep learning for time series classification: a review
Time Series Classification (TSC) is an important and challenging problem in
data mining. With the increase of time series data availability, hundreds of
TSC algorithms have been proposed. Among these methods, only a few have
considered Deep Neural Networks (DNNs) to perform this task. This is surprising
as deep learning has seen very successful applications in the last years. DNNs
have indeed revolutionized the field of computer vision especially with the
advent of novel deeper architectures such as Residual and Convolutional Neural
Networks. Apart from images, sequential data such as text and audio can also be
processed with DNNs to reach state-of-the-art performance for document
classification and speech recognition. In this article, we study the current
state-of-the-art performance of deep learning algorithms for TSC by presenting
an empirical study of the most recent DNN architectures for TSC. We give an
overview of the most successful deep learning applications in various time
series domains under a unified taxonomy of DNNs for TSC. We also provide an
open source deep learning framework to the TSC community where we implemented
each of the compared approaches and evaluated them on a univariate TSC
benchmark (the UCR/UEA archive) and 12 multivariate time series datasets. By
training 8,730 deep learning models on 97 time series datasets, we propose the
most exhaustive study of DNNs for TSC to date.Comment: Accepted at Data Mining and Knowledge Discover