  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.
