MyHeartCounts/openmhc-dlinear-fc

OpenMHC Forecasting — DLinear

Track 3 (forecasting) reference checkpoint for the MyHeartCounts / OpenMHC wearable-health benchmark.

This checkpoint is a DLinear model. DLinear is a lightweight linear forecaster that decomposes the series into trend and seasonal components and applies a separate linear projection to each.

Trained from scratch on the MHC training split using the PyPOTS implementation.

• Checkpoint format: PyPOTS checkpoint (model.pypots) + standard_scaler_stats.json + training_config.json
• Forecasting task: 24-hour-ahead, 19 sensor channels, hourly resolution.

Model & implementation

• PyPOTS toolkit (implementation)
• PyPOTS documentation
• Paper: Are Transformers Effective for Time Series Forecasting? (Zeng et al., AAAI 2023)

Usage

import openmhc
from openmhc.forecasters import DLinearForecaster

# pip install "openmhc[pypots]"
fc = DLinearForecaster.from_release("hf://MyHeartCounts/openmhc-dlinear-fc@v1.1")
results = openmhc.evaluate_forecasting(fc, version="full")

The same bundle also loads in the evaluation harness via model.release_dir=hf://MyHeartCounts/openmhc-dlinear-fc@v1.1. See openmhc_manifest.json for provenance (training run, base model, fine-tuning details) and architecture metadata.

Citation

If you use this checkpoint, please cite the OpenMHC benchmark and the original DLinear work (linked above).