Oura Ring Gen 4 sensor data — not clinical measurementsN=1 case study — not validated for clinical decisionsHEV diagnosed Mar 18; interpret findings cautiously in this Day 8 post-ruxolitinib window

Foundation Model Forecasting

Amazon Chronos-2 + ARIMA Statistical Baseline - Oura Ring Biometrics, Post-HSCT
Generated 2026-03-27 07:09 · Post-HSCT Patient
Foundation Model
Abnormal
unavailable
ModuleNotFoundError: No module named 'torch'
RMSSD MAE
Abnormal
N/Ams
90% PI coverage: N/A%
HR MAE
Abnormal
N/Abpm
90% PI coverage: N/A%
Feb 9 Chronos Holdout
Abnormal
Not run
Chronos retrospective holdout unavailable
Ruxolitinib Start
Info
16. mar 2026
March Ensemble
ARIMA only
0 HIGH confidence anomalies | Feb 9 not detected

1. Nightly RMSSD and HR - Chronos unavailable

Chronos could not be loaded in this run, so nightly foundation-model forecast charts were skipped. Statistical baseline outputs remain below.

2. Continuous HR - Chronos unavailable

Hourly Chronos analysis was skipped because the Chronos pipeline was unavailable.

3. Statistical Baseline - ARIMA

Chronos was unavailable, so the consensus view is reduced to the statistical baseline only.

4. Feb 9 Retrospective Validation

Retrospective Chronos validation was skipped because the foundation model was unavailable.

Feb 9 Detection Summary
MethodSeriesDetected?Residual
Chronos retrospective not available in this run.

Prospective March ensemble HIGH-confidence anomalies: None detected
Feb 9 in March ensemble consensus: No

5. Pre vs Post Ruxolitinib Regime Analysis

Pre/post-ruxolitinib Chronos regime analysis was skipped because the foundation model was unavailable.

Detailed Metrics

SectionMetricValue
data_range
start2026-01-04
end2026-03-23
n_nights79
ensemble_consensus
errorno overlapping forecasts
statistical_hr
modelARIMA(1, 1, 1)
mae5.015
rmse6.422
coverage_90ci92.900
n_anomalies1
anomaly_dates2026-03-10
statistical_rmssd
modelARIMA(0, 1, 1)
mae1.929
rmse2.341
coverage_90ci92.900
n_anomalies1
anomaly_dates2026-03-09
N=1 retrospective case study. All detection metrics are descriptive, not inferential. The model was trained and evaluated on a single patient's data. Validation requires an external multi-patient cohort.