There is an increasing demand to process streams of temporal data in energy-limited scenarios such as embedded devices, driven by the advancement and expansion of Internet of Things (IoT) and Cyber-Physical Systems (CPS). Spiking neural network has drawn attention as it enables low power consumption by encoding and processing information as sparse spike events, which can be exploited for event-driven computation. Recent works also show SNNs' capability to process spatial temporal information. Such advantages can be exploited by power-limited devices to process real-time sensor data. However, most existing SNN training algorithms focus on vision tasks and temporal credit assignment is not addressed. Furthermore, widely adopted rate encoding ignores temporal information, hence it's not suitable for representing time series. In this work, we present an encoding scheme to convert time series into sparse spatial temporal spike patterns. A training algorithm to classify spatial temporal patterns is also proposed. Proposed approach is evaluated on multiple time series datasets in the UCR repository and achieved performance comparable to deep neural networks.

中文翻译：

---

使用尖峰神经网络的多元时间序列分类

在物联网 (IoT) 和网络物理系统 (CPS) 的进步和扩展的推动下，在诸如嵌入式设备等能源受限的场景中，对处理时间数据流的需求不断增加。尖峰神经网络引起了人们的注意，因为它通过将信息编码和处理为稀疏尖峰事件来实现低功耗，可用于事件驱动计算。最近的工作还展示了 SNN 处理时空信息的能力。功率受限的设备可以利用这些优势来处理实时传感器数据。然而，大多数现有的 SNN 训练算法都专注于视觉任务，并且没有解决时间信用分配问题。此外，广泛采用的速率编码忽略了时间信息，因此不适合表示时间序列。在这项工作中，我们提出了一种将时间序列转换为稀疏时空尖峰模式的编码方案。还提出了一种对时空模式进行分类的训练算法。提议的方法在 UCR 存储库中的多个时间序列数据集上进行了评估，并获得了与深度神经网络相当的性能。