一种用于边缘在线时间序列预测的新型超维度计算框架
A Novel Hyperdimensional Computing Framework for Online Time Series Forecasting on the Edge
AI总结：
本文提出TSF-HD框架，将在线非线性时间序列预测转化为线性超维度预测，通过协同训练适应分布变化，实现快速、轻量且低延迟的在线预测，性能优于现有方法。
摘要：
近年来，针对时间序列预测，已开发出在线和离线深度学习模型。然而，离线深度预测模型无法有效适应时间序列数据的变化，而在线深度预测模型往往成本高昂且训练过程复杂。在本文中，我们将在线非线性时间序列预测问题重新构建为线性超维度时间序列预测问题。非线性低维时间序列数据被映射到高维（超维度）空间以进行线性超维度预测，从而实现快速、高效且轻量级的在线时间序列预测。我们的框架TSF-HD，通过一种新颖的协同训练框架，适应时间序列分布变化，该框架用于其超维度映射和线性超维度预测器。实验表明，TSF-HD在短期和长期时间序列预测中均优于现有技术水平，同时具有更低的推理延迟。我们的代码公开于http://github.com/tsfhd2024/tsf-hd.git。
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
