用多任务学习正则化NARX模型，提升时间序列预测泛化能力
Regularising NARX models with multi-task learning
- 构建同时预测当前与多个未来时刻的多任务NARX模型
- 高噪声下，多任务模型比独立模型降低归一化均方误差
- 适合需要稳定时序预测的工程应用，如控制系统建模
非线性自回归外生输入（NARX）模型可用于描述随时间变化的过程，其输出依赖于历史输出和当前/历史外部输入变量。但该模型易过拟合，导致未来预测泛化性能差。本文提出一种多任务学习（MTL）方法：在传统NARX基础上，同时预测当前时刻及多个未来时刻的输出，通过未来输出对当前输出进行正则化。实验表明，在高噪声条件下，该多任务模型的归一化均方误差（NMSE）优于独立学习器的对应结果。
原文摘要 · Abstract (English)
A Nonlinear Auto-Regressive with eXogenous inputs (NARX) model can be used to describe time-varying processes; where the output depends on both previous outputs and current/previous external input variables. One limitation of NARX models is their propensity to overfit and result in poor generalisation for future predictions. The proposed method to help to overcome the issue of overfitting is a NARX model which predicts outputs at both the current time and several lead times into the future. This is a form of multi-task learner (MTL); whereby the lead time outputs will regularise the current time output. This work shows that for high noise level, MTL can be used to regularise NARX with a lower Normalised Mean Square Error (NMSE) compared to the NMSE of the independent learner counterpart.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。