Probabilistic Time Series Forecasting with 🤗 Transformers

This post details the implementation and usage of the Time Series Transformer model within the Hugging Face Transformers library for univariate probabilistic time series forecasting. It covers environment setup, loading the `monash_tsf` dataset, defining the model architecture (Encoder-Decoder Transformer), handling data transformations with GluonTS (including `InstanceSplitter` and data loaders), performing a forward pass, training the model using ancestral sampling, and conducting inference. It highlights the benefits of global probabilistic models over local point forecasting methods and discusses the Transformer's suitability for time series data, including its handling of missing values and autoregressive generation.