  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.
