SPOT-VSA: Efficient Stability-Plasticity Tunable On-Line Time-Series Forecasting With Vector Symbolic Architectures

Online time-series forecasting requires rapid adaptation to non-stationary data while preserving previously acquired patterns, confronting the stability–plasticity dilemma. Current approaches struggle along this frontier, either suffering catastrophic forgetting through aggressive adaptation or incurring heavy computational overhead through redundant or frozen backbones, and none quantifies its position on the tradeoff. We propose SPOT-VSA, an efficient neuro-vector-symbolic architecture for task-free online forecasting. By projecting inputs into a latent space via Vector Symbolic Architectures, the model introduces an explicit inductive bias where short- and long-term temporal dependencies are bound into a single representation with negligible interference. Forecasts are produced by a hybrid sequence-to-sequence and autoregressive strategy whose granularity sets the model's position on the stability–plasticity frontier. We ground this claim theoretically, deriving bounds on stability and plasticity that identify forecasting granularity as a calibration knob and show that the step-size tradeoff between the two emerges only under distribution drift. Across diverse benchmarks and horizons, SPOT-VSA matches or surpasses state-of-the-art online forecasters in accuracy, exhibits the lowest forgetting among methods adapting their full model, and validates theoretical predictions on synthetic drift streams. In addition, it achieves up to $404\times$ speedup and $390\times$ higher energy efficiency on edge devices, with up to $46\times$ fewer parameters.We will make our code available after paper acceptance.