A Novel Hyperdimensional Computing Framework for Online Time Series
  Forecasting on the Edge
Mohamed Mejri Chandramouli Amarnath Abhijit Chatterjee
In recent years, both online and offline deep learning models have been
developed for time series forecasting. However, offline deep forecasting models
fail to adapt effectively to changes in time-series data, while online deep
forecasting models are often expensive and have complex training procedures. In
this paper, we reframe the online nonlinear time-series forecasting problem as
one of linear hyperdimensional time-series forecasting. Nonlinear
low-dimensional time-series data is mapped to high-dimensional
(hyperdimensional) spaces for linear hyperdimensional prediction, allowing
fast, efficient and lightweight online time-series forecasting. Our framework,
TSF-HD, adapts to time-series distribution shifts using a novel co-training
framework for its hyperdimensional mapping and its linear hyperdimensional
predictor. TSF-HD is shown to outperform the state of the art, while having
reduced inference latency, for both short-term and long-term time series
forecasting. Our code is publicly available at
http://github.com/tsfhd2024/tsf-hd.git