Multivariate Time Series Forecasting with Transformers

Many real-world applications of Machine Learning involve making predictions about the outcomes of a group of related variables based on historical context. We might want to forecast the traffic conditions on connected roads, the weather at nearby locations, or the demand for similar products. By modeling multiple time series together, we hope that changes in one variable may reveal key information about the behavior of related variables. Multivariate Time Series Forecasting (TSF) datasets have two axes of difficulty: we need to learn temporal relationships to understand how values change over time and spatial relationships to know how variables impact one another. Popular statistical approaches to TSF can struggle to interpret long context sequences and scale to complex variable relationships.