AI BriefingHow to Use the PatchTST Time-Series Model
Key point
This explains the structure of PatchTST, a model that improves time-series forecasting performance, and how to use it with the Hugging Face library.
Details
PatchTST is a model that splits time-series data into patch units before feeding them into a Transformer, based on research presented at ICLR 2023.
The key core components are as follows:
- Patch Segmentation: Time series are divided into sub-series-level patches, which are used as input tokens for the Transformer.
- Channel-independence: Each channel is treated as a univariate time series, adopting a global univariate model approach in which all series share the same embedding and Transformer weights.
This design provides three main benefits:
- It preserves local semantic information during the embedding process.
- Through striding between patches, it dramatically reduces the computation and memory usage of the attention map.
- By adjusting between patch length and context length, it enables reference to a longer history.
Additionally, PatchTST has a modular structure that supports both masked time-series pre-training and direct time-series forecasting.
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