A Novel Hyperdimensional Computing Framework for Online Time Series Forecasting on the Edge
<|reference_start|>A Novel Hyperdimensional Computing Framework for Online Time Series Forecasting on the Edge: 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<|reference_end|>
@article{mejri2024a,
    title={A Novel Hyperdimensional Computing Framework for Online Time Series
  Forecasting on the Edge},
    author={Mohamed Mejri, Chandramouli Amarnath, Abhijit Chatterjee},
    journal={arXiv preprint arXiv:2402.01999},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.01999},
    primaryClass={cs.LG cs.AI}
}