Energy and data-efficient online time series prediction for predicting evolving dynamical systems are critical in several fields, especially edge AI applications that need to update continuously based on streaming data. However, current DNN-based supervised online learning models require a large amount of training data and cannot quickly adapt when the underlying system changes. Moreover, these models require continuous retraining with incoming data making them highly inefficient. To solve these issues, we present a novel Continuous Learning-based Unsupervised Recurrent Spiking Neural Network Model (CLURSNN), trained with spike timing dependent plasticity (STDP). CLURSNN makes online predictions by reconstructing the underlying dynamical system using Random Delay Embedding by measuring the membrane potential of neurons in the recurrent layer of the RSNN with the highest betweenness centrality. We also use topological data analysis to propose a novel methodology using the Wasserstein Distance between the persistence homologies of the predicted and observed time series as a loss function. We show that the proposed online time series prediction methodology outperforms state-of-the-art DNN models when predicting an evolving Lorenz63 dynamical system.

翻译：在预测演化动态系统方面，能量高效且数据高效的在线时间序列预测在多个领域至关重要，特别是需要基于流式数据持续更新的边缘AI应用。然而，当前基于深度神经网络的有监督在线学习模型需要大量训练数据，且当底层系统发生变化时无法快速适应。此外，这些模型需要用新输入数据持续重训练，导致效率低下。为解决这些问题，我们提出一种基于连续学习的无监督递归脉冲神经网络模型（CLURSNN），并通过脉冲时序依赖可塑性（STDP）进行训练。CLURSNN通过测量递归脉冲神经网络（RSNN）递归层中具有最高介数中心度的神经元膜电位，利用随机延迟嵌入重构底层动态系统，从而实现在线预测。我们还利用拓扑数据分析提出一种新颖方法，将预测时间序列与观测时间序列的持续同调之间的Wasserstein距离作为损失函数。实验表明，在预测演化的Lorenz63动态系统时，所提出的在线时间序列预测方法优于现有最优深度神经网络模型。