This PR introduces support for the PatchTSMixer model in the Plato federated learning framework for time series forecasting tasks.

Description

Specifically, this PR:

• Added ETT.py to support the Electricity Transformer dataset, including data loading, preprocessing, and federated partitioning logic.
• Integrated the PatchTSMixer model architecture in HuggingFace for time series forecasting within Plato.
• Added TOML configuration files for PatchTSMixer experiments under configs/TimeSeries/.
• Added mean squared error (MSE)–based evaluation for PatchTSMixer experiments.

How has this been tested?

Quick check evaluation:

uv run python plato.py --config configs/TimeSeries/patchtsmixer_custom.toml

This configuration runs only 3 rounds, which is useful for quick functional tests and CORE-style checks. The run completed successfully without runtime errors.

Longer training run:

uv run python plato.py --config configs/TimeSeries/patchtsmixer_large.toml

This configuration uses more rounds. After 400 rounds, the MSE dropped from 7.14 to around 1.30, indicating that the model and data pipeline are working as expected.

Types of changes

• Bug fix (non-breaking change which fixes an issue) Fixes #
• New feature (non-breaking change which adds functionality)
• Breaking change (fix or feature that would cause existing functionality to not work as expected)

Checklist:

• My code has been formatted using the Ruff formatter (ruff format) and checked using the Ruff linter (ruff check --fix).
• My change requires a change to the documentation.
• I have updated the documentation accordingly.