  Time series analysis and modelling constitute a crucial research area.
Traditional artificial neural networks struggle with complex, non-stationary
time series data due to high computational complexity, limited ability to
capture temporal information, and difficulty in handling event-driven data. To
address these challenges, we propose a Multi-modal Time Series Analysis Model
Based on Spiking Neural Network (MTSA-SNN). The Pulse Encoder unifies the
encoding of temporal images and sequential information in a common pulse-based
representation. The Joint Learning Module employs a joint learning function and
weight allocation mechanism to fuse information from multi-modal pulse signals
complementary. Additionally, we incorporate wavelet transform operations to
enhance the model's ability to analyze and evaluate temporal information.
Experimental results demonstrate that our method achieved superior performance
on three complex time-series tasks. This work provides an effective
event-driven approach to overcome the challenges associated with analyzing
intricate temporal information. Access to the source code is available at
https://github.com/Chenngzz/MTSA-SNN}{https://github.com/Chenngzz/MTSA-SNN
