论文标题
时间序列预测的时间序列学习
Few-shot Learning for Time-series Forecasting
论文作者
论文摘要
时间序列预测对于许多应用很重要。通常在特定目标任务中使用时间序列数据对预测模型进行培训。但是，目标任务中的足够数据可能不可用，从而导致性能降解。在本文中，我们提出了一种几次学习方法，该方法预测目标任务中几个时间序列的目标任务中时间序列的未来值。我们的模型是使用与目标任务不同的多个培训任务中的时间序列数据培训的。我们的模型使用一些时间序列来构建基于具有注意机制的复发神经网络的预测函数。使用注意机制，我们可以在当前情况下以少量时间序列的形式检索有用的模式。我们的模型通过最小化预测下一个时间步度值的预期测试误差来训练。我们使用90个时间序列数据集证明了该方法的有效性。
Time-series forecasting is important for many applications. Forecasting models are usually trained using time-series data in a specific target task. However, sufficient data in the target task might be unavailable, which leads to performance degradation. In this paper, we propose a few-shot learning method that forecasts a future value of a time-series in a target task given a few time-series in the target task. Our model is trained using time-series data in multiple training tasks that are different from target tasks. Our model uses a few time-series to build a forecasting function based on a recurrent neural network with an attention mechanism. With the attention mechanism, we can retrieve useful patterns in a small number of time-series for the current situation. Our model is trained by minimizing an expected test error of forecasting next timestep values. We demonstrate the effectiveness of the proposed method using 90 time-series datasets.
