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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Reservoir Computing Approaches for Representation and Classification of Multivariate Time Series
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Reservoir Computing Approaches for Representation and Classification of Multivariate Time Series

机译：多变量时间序列表示和分类的储层计算方法

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摘要

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 article, 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 with other RC methods, our model space yields better representations and attains comparable computational performance due 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 with other MTS classifiers, including deep learning models and time series kernels. Results obtained on the benchmark and real-world MTS data sets show that RC classifiers are dramatically faster and, when implemented using our proposed representation, also achieve superior classification accuracy.
机译：多变量时间序列（MTS）的分类已被各种各样的方法解决并应用于各种场景。储库计算（RC）提供有效的工具来生成可以通过标准分类器进一步处理的MTS的矢量，固定尺寸表示。尽管其无与伦比的训练速度，但基于标准RC架构的MTS分类器无法实现完全培训的神经网络的相同准确性。在本文中，我们介绍了储层模型空间，这是一种无监督的方法，基于RC学习MTS的矢量表示。每个MTS在培训的线性模型的参数内被编码，以预测储存器动力学的低维嵌入。与其他RC方法相比，我们的模型空间会产生更好的表示，并且由于中间维度减少过程而获得了可比的计算性能。作为第二种贡献，我们为MTS分类提出了一个模块化的RC框架，其中一个相关的开源Python库。该框架提供了不同的模块来无缝实现高级RC架构。与其他MTS分类器进行比较，包括深度学习模型和时间序列内核。在基准和现实世界MTS数据集中获得的结果表明，RC分类器的速度较快，并且在使用我们提出的表示实施时，也实现了卓越的分类准确性。

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