论文标题
学习时间序列预测的深度时间指数模型
Learning Deep Time-index Models for Time Series Forecasting
论文作者
论文摘要
深度学习已被积极地应用于时间序列预测，从而导致了属于历史值模型类别的新方法。然而，尽管时间指数模型具有吸引人的属性，例如能够建模基础时间序列动态的连续性质，但对它们的关注很少。确实，虽然天真的深度时间指数模型比经典时间指数模型的手动预定义功能表示表达得多，但由于缺乏电感偏见，它们无法预测，无法推广到不见了的时间步骤。在本文中，我们提出了DeepTime，即学习深度时间指数模型的元优化框架，以克服这些局限性，从而产生有效，准确的预测模型。长序列时间序列预测设置的现实世界数据集进行了广泛的实验表明，我们的方法通过最先进的方法实现了竞争结果，并且非常有效。代码可从https://github.com/salesforce/deeptime获得。
Deep learning has been actively applied to time series forecasting, leading to a deluge of new methods, belonging to the class of historical-value models. Yet, despite the attractive properties of time-index models, such as being able to model the continuous nature of underlying time series dynamics, little attention has been given to them. Indeed, while naive deep time-index models are far more expressive than the manually predefined function representations of classical time-index models, they are inadequate for forecasting, being unable to generalize to unseen time steps due to the lack of inductive bias. In this paper, we propose DeepTime, a meta-optimization framework to learn deep time-index models which overcome these limitations, yielding an efficient and accurate forecasting model. Extensive experiments on real world datasets in the long sequence time-series forecasting setting demonstrate that our approach achieves competitive results with state-of-the-art methods, and is highly efficient. Code is available at https://github.com/salesforce/DeepTime.
