论文标题
使用可区分遗忘的时间序列预测进行分配转移
Time Series Prediction under Distribution Shift using Differentiable Forgetting
论文作者
论文摘要
时间序列预测通常会因分布变化而复杂化，这需要自适应模型以适应时间变化的分布。我们将分配转移的时间序列预测作为加权经验风险最小化问题。经验风险中先前观察结果的加权取决于遗忘机制，该机制控制了用于估计预测模型的相关性和有效样本量之间的权衡。与以前的工作相反，我们为遗忘机制参数提出了一种基于梯度的学习方法。这加快了优化的速度，因此允许更具表现力的遗忘机制。
Time series prediction is often complicated by distribution shift which demands adaptive models to accommodate time-varying distributions. We frame time series prediction under distribution shift as a weighted empirical risk minimisation problem. The weighting of previous observations in the empirical risk is determined by a forgetting mechanism which controls the trade-off between the relevancy and effective sample size that is used for the estimation of the predictive model. In contrast to previous work, we propose a gradient-based learning method for the parameters of the forgetting mechanism. This speeds up optimisation and therefore allows more expressive forgetting mechanisms.
