Empirical Study of Padding Methods and Resource Efficiency in Replay-Based Class-Incremental Continual Learning for Variable-Length Time Series

Fixed length time series data are seldom found in real world datasets. In cases such as language, wildlife, and machining processes, data can vary in length. Similar to real-world scenarios, it is not always feasible to retrain a machine learning (ML) model to handle new and old data simultaneously. Continual learning (CL) was introduced to adapt ML systems to new data while retaining performance on past information and minimizing large training runs. Replay-based methods which use a memory buffer to augment incoming data have emerged as the preeminent approach type for CL. While CL has been applied to many scenarios including time series and images, their performance has yet to be evaluated on variable-length time series (VLTS) data such as handwriting trajectories, industrial sensing, insect wingbeat spectrograms, and spoken digits. When implementing CL-ML systems, factors affecting performance and implementation cost are important research areas in ML. This article reports an empirical comparison of padding choices, ML models, CL methods, replay buffer size, computational cost, and accuracy on four VLTS datasets. The role of padding in transforming VLTS is examined, with experiments showing different mechanisms affect CL methods dissimilarly with padding responsible for an average of 32.3% accuracy range within CL methods. The computational load-accuracy tradeoff analysis shows that traditional methods such as a multi-layer perceptron coupled with experience replay and a small buffer improved memory and energy-accuracy efficiency by 111.5% and 14.6%, respectively.