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.

采用水库计算方法的多元时间序列分类技术，基于线性模型训练产生 MTS 的低维嵌入的参数来编码每个 MTS，应用时附带中间的维度重组程序，以及提供不同的模块以轻松创建高级的 RC 架构的增量式 RC 框架，比其他 MTS 分类器更快，还能实现更高的分类精度。