  Real-world time series often exhibit complex interdependencies that cannot be
captured in isolation. Global models that model past data from multiple related
time series globally while producing series-specific forecasts locally are now
common. However, their forecasts for each individual series remain isolated,
failing to account for the current state of its neighbouring series.
Multivariate models like multivariate attention and graph neural networks can
explicitly incorporate inter-series information, thus addressing the
shortcomings of global models. However, these techniques exhibit quadratic
complexity per timestep, limiting scalability. This paper introduces the
Context Neural Network, an efficient linear complexity approach for augmenting
time series models with relevant contextual insights from neighbouring time
series without significant computational overhead. The proposed method enriches
predictive models by providing the target series with real-time information
from its neighbours, addressing the limitations of global models, yet remaining
computationally tractable for large datasets.
