In this paper, we introduce Masked Multi-Step Multivariate Forecasting (MMMF), a novel and general self-supervised learning framework for time series forecasting with known future information. In many real-world forecasting scenarios, some future information is known, e.g., the weather information when making a short-to-mid-term electricity demand forecast, or the oil price forecasts when making an airplane departure forecast. Existing machine learning forecasting frameworks can be categorized into (1) sample-based approaches where each forecast is made independently, and (2) time series regression approaches where the future information is not fully incorporated. To overcome the limitations of existing approaches, we propose MMMF, a framework to train any neural network model capable of generating a sequence of outputs, that combines both the temporal information from the past and the known information about the future to make better predictions. Experiments are performed on two real-world datasets for (1) mid-term electricity demand forecasting, and (2) two-month ahead flight departures forecasting. They show that the proposed MMMF framework outperforms not only sample-based methods but also existing time series forecasting models with the exact same base models. Furthermore, once a neural network model is trained with MMMF, its inference speed is similar to that of the same model trained with traditional regression formulations, thus making MMMF a better alternative to existing regression-trained time series forecasting models if there is some available future information.
翻译:在本文中,我们引入了隐蔽的多标准多变量预测(MMMF),这是一个用于根据已知的未来信息进行时间序列预测的新颖和一般的自我监督的自我监督学习框架(MMMF),这是一个用于利用已知的未来信息进行时间序列预测的新颖和普遍自我监督的学习框架;在许多现实世界预测假设中,一些未来的信息是已知的,例如,在作出短期到中期电力需求预测时天气信息,或在作出飞机起飞预测时石油价格预测;现有的机器学习预测框架可以分为:(1) 以抽样为基础的方法,每个预测都是独立进行的;(2) 未来信息没有完全纳入的时序回归方法。为了克服现有方法的局限性,我们建议MMMFMF,这是一个框架,用来培训任何能够产生一系列产出的神经网络模型,这种模型既包括过去的时间信息,也包括已知的未来信息,以作出更好的预测。 对现有电力需求中期预测的两种真实世界数据集进行了实验,(2) 提前两个月的航班起飞预测。它们表明,拟议的MMMF框架不仅优于基于抽样的方法,而且现有时间序列模型也优于现有的模型,因此,其传统预测速度与现有的基准模型是相同的,一旦经过培训的模型与现有的模型是相同的,则与现有的,而具有相同的,则具有类似的,则具有相同的,则与现在的。