A decoder-only foundation model for time-series forecasting

Posted by Rajat Sen and Yichen Zhou, Google Research

Time-series forecasting is ubiquitous in various domains, such as retail, finance, manufacturing, healthcare and natural sciences. In retail use cases, for example, it has been observed that improving demand forecasting accuracy can meaningfully reduce inventory costs and increase revenue. Deep learning (DL) models have emerged as a popular approach for forecasting rich, multivariate, time-series data because they have proven to perform well in a variety of settings (e.g., DL models dominated …

accuracy cases costs decoder deep learning demand demand forecasting domains example finance forecasting foundation foundation model google google cloud platform google research healthcare inventory machine learning manufacturing natural reduce research retail revenue series use cases