Advancing Online Continual Learning for Time Series Modelling

In this seminar, I explored online continual learning using the recurrent RTS smoother with TAGI-LSTM. Experiments on synthetic data were presented as a proof of concept for methods that enable BRNN-based architectures to learn online and adapt continuously to dynamic environments.

The approach uses interventions on model parameters through the addition of noise. Future directions included optimizing the smoothing window length and calibrating noise injection into the parameters.

Source: CIV-ML’s Seminars