论文标题
时间序列预测（TSF）使用各种深度学习模型
Time Series Forecasting (TSF) Using Various Deep Learning Models
论文作者
论文摘要
时间序列预测（TSF）用于根据以前的时间点的学习来预测未来时间点的目标变量。为了使问题可解决，学习方法过去使用固定长度窗口的数据作为明确的输入。在本文中，我们研究了预测模型的性能如何随着不同外观的窗口大小的函数而变化，以及预测未来的不同时间。我们还考虑了最近基于注意力的变压器模型的性能，该模型在图像处理和自然语言处理领域取得了良好的成功。总的来说，我们将四种不同的深度学习方法（RNN，LSTM，GRU和Transform）与基线方法进行了比较。我们使用的数据集（每小时）是UCI网站上的北京空气质量数据集，其中包括一个多元时间序列，这些时间序列是每小时5年（2010 - 14年）测量的许多因素。对于每个模型，我们还报告性能与外观背包窗口大小之间的关系以及未来预测的时间点的数量。我们的实验表明，对于我们的大多数单步和多步预测，变压器模型具有最佳性能，其平均平均误差（MAE = 14.599，23.273）和根平方误差（RSME = 23.573，38.131）。外观背包窗口的最佳尺寸可以预测未来1小时的时间，而2或4天的最佳尺寸可以预测未来3个小时的最佳状态。
Time Series Forecasting (TSF) is used to predict the target variables at a future time point based on the learning from previous time points. To keep the problem tractable, learning methods use data from a fixed length window in the past as an explicit input. In this paper, we study how the performance of predictive models change as a function of different look-back window sizes and different amounts of time to predict into the future. We also consider the performance of the recent attention-based Transformer models, which has had good success in the image processing and natural language processing domains. In all, we compare four different deep learning methods (RNN, LSTM, GRU, and Transformer) along with a baseline method. The dataset (hourly) we used is the Beijing Air Quality Dataset from the UCI website, which includes a multivariate time series of many factors measured on an hourly basis for a period of 5 years (2010-14). For each model, we also report on the relationship between the performance and the look-back window sizes and the number of predicted time points into the future. Our experiments suggest that Transformer models have the best performance with the lowest Mean Average Errors (MAE = 14.599, 23.273) and Root Mean Square Errors (RSME = 23.573, 38.131) for most of our single-step and multi-steps predictions. The best size for the look-back window to predict 1 hour into the future appears to be one day, while 2 or 4 days perform the best to predict 3 hours into the future.
