  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.
