MTSA-SNN: A Multi-modal Time Series Analysis Model Based on Spiking Neural Network
This work addresses challenges in time series analysis for applications dealing with event-driven data, though it appears incremental as it builds on existing spiking neural network approaches.
The authors tackled the problem of analyzing complex, non-stationary time series data by proposing MTSA-SNN, a multi-modal model based on spiking neural networks, which achieved superior performance on three complex time-series tasks.
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