Spatiotemporal-Linear: Towards Universal Multivariate Time Series
  Forecasting
Aiyinsi Zuo Haixi Zhang Zirui Li Ce Zheng
Within the field of complicated multivariate time series forecasting (TSF),
popular techniques frequently rely on intricate deep learning architectures,
ranging from transformer-based designs to recurrent neural networks. However,
recent findings suggest that simple Linear models can surpass sophisticated
constructs on diverse datasets. These models directly map observation to
multiple future time steps, thereby minimizing error accumulation in iterative
multi-step prediction. Yet, these models fail to incorporate spatial and
temporal information within the data, which is critical for capturing patterns
and dependencies that drive insightful predictions. This oversight often leads
to performance bottlenecks, especially under specific sequence lengths and
dataset conditions, preventing their universal application. In response, we
introduce the SpatioTemporal-Linear (STL) framework. STL seamlessly integrates
time-embedded and spatially-informed bypasses to augment the Linear-based
architecture. These extra routes offer a more robust and refined regression to
the data, particularly when the amount of observation is limited and the
capacity of simple linear layers to capture dependencies declines. Empirical
evidence highlights STL's prowess, outpacing both Linear and Transformer
benchmarks across varied observation and prediction durations and datasets.
Such robustness accentuates its suitability across a spectrum of applications,
including but not limited to, traffic trajectory and rare disease progression
forecasting. Through this discourse, we not only validate the STL's distinctive
capacities to become a more general paradigm in multivariate time-series
prediction using deep-learning techniques but also stress the need to tackle
data-scarce prediction scenarios for universal application. Code will be made
available.