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Classifying functional time series

机译：分类功能时间序列

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摘要

We consider the problem of classifying a high-dimensional time series into a number of disjoint classes defined by training data. Techniques of this type are an important component of a number of emerging technologies. These include the use of dense sensor arrays for condition monitoring, brain-computer interfaces for communications and control, the detection of moving pedestrians from sequences of images and the study of cognitive function using high-resolution electroencephalography (EEG). We propose a novel approach to problems of this type using the parameters of an underlying functional auto-regression model. We compare the performance of this approach using two contrasting data sets. The first is based on simulated series with different characteristics and sampling schemes and a second based on high-dimensional times series generated by multi-channel EEG. Both experiments show that our approach outperforms conventional time series methods by exploiting low-intrinsic dimensionality (smoothness). In addition, our simulation experiments show that good performance can be maintained for data generated by non-stationary sampling schemes, the latter causing large reductions in the performance of conventional procedures. These experiments suggest that meaningful information can be extracted from high-resolution EEG.
机译：我们考虑将高维时间序列分类为由训练数据定义的许多不相交的类的问题。这种技术是许多新兴技术的重要组成部分。这些措施包括使用密集的传感器阵列进行状态监测，用于通讯和控制的脑机接口，从图像序列中检测出行人，以及使用高分辨率脑电图（EEG）研究认知功能。我们使用基础功能自回归模型的参数为此类问题提出了一种新颖的方法。我们使用两个对比数据集来比较这种方法的性能。第一个基于具有不同特性和采样方案的模拟序列，第二个基于多通道EEG生成的高维时间序列。这两个实验都表明，我们的方法通过利用低内在维数（平滑度）优于常规时间序列方法。此外，我们的仿真实验表明，对于非平稳采样方案生成的数据可以保持良好的性能，后者会导致常规程序的性能大大降低。这些实验表明，可以从高分辨率脑电图中提取有意义的信息。

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