  Motivated by recent advances in large language models for Natural Language
Processing (NLP), we design a time-series foundation model for forecasting
whose out-of-the-box zero-shot performance on a variety of public datasets
comes close to the accuracy of state-of-the-art supervised forecasting models
for each individual dataset. Our model is based on pretraining a
patched-decoder style attention model on a large time-series corpus, and can
work well across different forecasting history lengths, prediction lengths and
temporal granularities.
