UKF-HD: Unscented Kalman Filtering in Hyperdimensional Computing for Robust On-Device IoT Forecasting - de S. Ferreira, Adriano; S. de Araújo, Leandro (Anais Estendidos do Simpósio Brasileiro de Banco de Dados (SBBD); 2026: Anais Estendidos do XLI Simpósio Brasileiro de Bancos de Dados, 2026)
Time series forecasting is moving to the edge, where models run on small IoT devices and must cope with noisy sensor streams. Hyperdimensional Computing (HDC) fits this setting, since it trains in a single pass using cheap, parallel operations. Current HDC forecasters, however, either use a simplistic delta rule or a linear Kalman Filter that assumes Gaussian noise and keeps a costly D × D covariance. We propose UKF-HD, the first formulation of the Unscented Kalman Filter inside HDC. The HDC prediction is linear in the weights, so the nonlinearity lies in the encoder; we therefore apply the unscented transform to the input window and push it through that encoder. The resulting gain accounts for the encoder nonlinearity and replaces the D × D covariance with a small p × p uncertainty over the input. We also propose GradHD, a gradient-based HDC forecaster, and its combination with the unscented front-end (GradHD+UKF). On multi-sensor IoT datasets corrupted by Gaussian, missing-value and Poisson noise, UKF-HD is more robust than the linear Kalman gain while using far less memory.