Spatio-Temporal Forecasting: A Survey of Data-Driven Models Using Exogenous Data

Abstract

Forecasting Spatio-Temporal processes has been attracting a great deal of interest within the research community. In this context, there is an increasing trend of proposing and improving methodologies to gather and use vast amounts of Spatio-Temporal data. Spatio Temporal Forecasting (STF) problems present complex interactions and non-linearities as the temporal dimension and the spatial one are usually entangled. To address these problems, statistical, Machine Learning based, and Deep Learning based models are introduced and developed. The use of exogenous data has proven to be beneficial in many STF models. Various techniques of incorporating exogenous data in STF problems have been proposed in the literature. This survey aims at providing a systematic review of the data-driven STF models, with a focus on those that incorporate exogenous data. We first investigate the data properties, including their dynamics, types and representations. Next, we propose a new taxonomy of the reviewed models and inspect the different complexities of STF problems. Exogenous data incorporation techniques are then presented and analyzed. We conclude our paper by highlighting the current open challenges and future research directions.

Keywords

• Exogenous data
• deep learning
• forecasting
• machine learning
• spatio-temporal data
• statistics