Patch Time Series Transformer in Hugging Face

What changed

PatchTST on the Electricity data. We will then demonstrate the transfer learning capability of PatchTST by using the previously trained model to do zero-shot forecasting on the electrical transformer (ETTh1) dataset. The zero-shot forecasting performance will denote the test performance of the model in the target domain, without any training on the target domain.

Why it matters

A concrete addition to Practical AI: Tools, Models & Frameworks: it changes what's available to builders today rather than being general commentary.

How it compares

Related prior coverage to compare against:

• Habana Labs and Hugging Face Partner to Accelerate Transformer Model Training
• Introducing Hugging Face for Education 🤗
• Introducing Decision Transformers on Hugging Face 🤗

• Habana Labs and Hugging Face Partner to Accelerate Transformer Model Training similarity 0.32
• Introducing Hugging Face for Education 🤗 similarity 0.31
• Introducing Decision Transformers on Hugging Face 🤗 similarity 0.28