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：自感知是自主系统（例如自动驾驶网络）的关键能力，它依赖于高效的时间序列预测算法，使系统能够推演环境的未来状态，以及随时间推移对系统行为的影响。近年来，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态与空间依赖关系。尽管这些方案相较于统计方法展现出显著优势，但一个悬而未决的问题是如何通过时间序列中的时间分量，有效整合表征季节模式的全局信息，以提升预测模型的准确性。本文提出了一种将时间分量融入预测模型的通用方法。其核心思想是采用条件神经场表示从时间分量中提取的辅助特征，从而获取全局信息，并通过分层门控融合模块与自回归神经网络提取的局部信息实现有效整合。在道路交通与蜂窝网络流量数据集上的大量实验证明了所提方法的有效性。