Self-awareness is the key capability of autonomous systems, e.g., autonomous driving network, which relies on highly efficient time series forecasting algorithm to enable the system to reason about the future state of the environment, as well as its effect on the system behavior as time progresses. Recently, a large number of forecasting algorithms using either convolutional neural networks or graph neural networks have been developed to exploit the complex temporal and spatial dependencies present in the time series. While these solutions have shown significant advantages over statistical approaches, one open question is to effectively incorporate the global information which represents the seasonality patterns via the time component of time series into the forecasting models to improve their accuracy. This paper presents a general approach to integrating the time component into forecasting models. The main idea is to employ conditional neural fields to represent the auxiliary features extracted from the time component to obtain the global information, which will be effectively combined with the local information extracted from autoregressive neural networks through a layer-wise gated fusion module. Extensive experiments on road traffic and cellular network traffic datasets prove the effectiveness of the proposed approach.

翻译：自我意识是自主系统（例如自动驾驶网络）的关键能力，其依赖于高效的时间序列预测算法，使系统能够推理环境的未来状态，以及随时间推移对系统行为的影响。近期，大量利用卷积神经网络或图神经网络的预测算法被开发出来，以挖掘时间序列中复杂的时空依赖性。尽管这些解决方案相比统计方法展现出显著优势，但一个开放性问题是如何通过时间序列的时间分量，有效将代表季节模式的全局信息纳入预测模型以提高精度。本文提出了一种将时间分量融入预测模型的通用方法。核心思想是利用条件神经场表示从时间分量中提取的辅助特征以获取全局信息，并通过分层门控融合模块，将其与自回归神经网络提取的局部信息有效结合。在道路交通和蜂窝网络流量数据集上的大量实验证明了该方法的有效性。