论文标题
时间序列预测的深度变压器模型：流行率案例
Deep Transformer Models for Time Series Forecasting: The Influenza Prevalence Case
论文作者
论文摘要
在本文中，我们提出了一种新的时间序列预测方法。时间序列数据在许多科学和工程学科中都是普遍的。时间序列预测是建模时间序列数据的关键任务，并且是机器学习的重要领域。在这项工作中，我们开发了一种新颖的方法，该方法采用基于变压器的机器学习模型来预测时间序列数据。这种方法是通过利用自我注意的机制来学习时间序列数据的复杂模式和动态的作用。此外，它是一个通用框架，可以应用于单变量和多变量时间序列数据以及时间序列嵌入。使用类似流感的疾病（ILI）预测作为案例研究，我们表明我们的方法产生的预测结果与最新的艺术品相当。
In this paper, we present a new approach to time series forecasting. Time series data are prevalent in many scientific and engineering disciplines. Time series forecasting is a crucial task in modeling time series data, and is an important area of machine learning. In this work we developed a novel method that employs Transformer-based machine learning models to forecast time series data. This approach works by leveraging self-attention mechanisms to learn complex patterns and dynamics from time series data. Moreover, it is a generic framework and can be applied to univariate and multivariate time series data, as well as time series embeddings. Using influenza-like illness (ILI) forecasting as a case study, we show that the forecasting results produced by our approach are favorably comparable to the state-of-the-art.
