Yformer: U-Net Inspired Transformer Architecture for Far Horizon Time Series Forecasting

Understand

Time series data is ubiquitous in research as well as in a wide variety of industrial applications.

• Effectively analyzing the available historical data and providing insights into the far future allows us to make effective decisions.
• Recent research has witnessed the superior performance of transformer-based architectures, especially in the regime of far horizon time series forecasting.
• However, the current state of the art sparse Transformer architectures fail to couple down- and upsampling procedures to produce outputs in a similar resolution as the input.