Show HN: I have trained 15 blood glucose forecasting models on my own data

A developer with type 1 diabetes released T1DMAI, a repository benchmarking 15 blood glucose forecasting models — 22 configurations in total — on personal continuous glucose monitor data and the public MetaboNet dataset, evaluated using the Jacobs et al. recommendations published in IEEE Reviews in Biomedical Engineering in 2024. The developer's own 31,000-parameter D16 DILATE transformer with a 3,771-parameter LoRA adapter ranked second of 22 on personal data at 22.8 versus XGBoost's 22.2, and seventh on MetaboNet at 34.6 while posting the lowest RMSE of all models at 90 minutes; the LoRA adapter improved 60-minute error from 26.1 to 22.8 and 27.1 to 23.5 on personal data and from 36.6 to 34.6 and 36.7 to 35.1 on MetaboNet, but worsened D16 DILATE at 120 minutes on MetaboNet from 48.0 to 50.4. The repository states it is a research artifact only, not a medical device, with no regulatory clearance, and must not be used to calculate or adjust insulin doses.

Note Most of the code in this repo was written by AI. If you don't like that, feel free to leave. This repo is a zoo. Here I train & test many blood glucose forecasting models including my own on both my own data and metabonet public . My model is a small transformer that forecasts blood glucose 2 hours ahead. I've made two variants: D16 DILATE and D16 MSE which use DILATE and MSE loss for training, respectively. D16 refers to the width of the model 16 . They are both trained on synthetic traces, and then a LoRA adapter is added for light fine-tuning, but I also test them without the adapter. Feast thine eyes on some of their forecasts these have been trained on synthetic traces and no adapter is used for predictions : Caution Research and educational use only - not a medical device. T1DMAI is a research artifact. It is not clinically validated, has no regulatory clearance, and its output is a forecast of research signals, not medical advice. It must not be used to make medical, diagnostic, or treatment decisions, to calculate or adjust insulin doses, or to manage diabetes in any way. For medical guidance consult a qualified healthcare professional. The software is provided "as is", without warranty of any kind see LICENSE https://github.com/0xdeadf1sh/T1DMAI/blob/main/LICENSE . I have type 1 diabetes and I wanna know if I can train a BG forecasting model to help me with my daily management. To this end I've made some auxiliary tools: - T1DMSIM https://github.com/0xdeadf1sh/T1DMSIM creates synthetic CGM traces since UVA/Padova was too slow on my laptop. - T1DMDROID https://github.com/0xdeadf1sh/T1DMDROID runs the models on my phone, also has some neat functionality like reading BG from multiple CGMs at once. For evaluation, I use the recommendations by Jacobs et al., IEEE Reviews in Biomedical Engineering, 2024. It's a nice paper that you should probably read. All models suck. XGBoost is the best one but my own model comes close. More details below: 1. On my data, D16 DILATE + LoRA comes 2nd of 22 22.8 . Wilcoxon puts it level with XGBoost 22.2 at 60 and 90 min, behind it at 30 and 120. 2. On MetaboNet, D16 DILATE + LoRA comes 7th 34.6 but has the lowest RMSE of all models at 90 min. XGBoost leads at 30, 60 and 120. 3. At 60 min a 3771 parameter LoRA adapter helps both models on both datasets: 26.1 to 22.8 and 27.1 to 23.5 on my data, 36.6 to 34.6 and 36.7 to 35.1 on MetaboNet. At 120 min on MetaboNet it makes D16 DILATE worse 48.0 to 50.4 . 4. On MetaboNet, LoRA also fixes the bands. The 90% band holds the truth 75-78% of the time at 60 min with sim only and 81-85% with LoRA. On my data both are 83-85%. 5. T1DMAI + LoRA reacts earliest on my data: 40-50 min time gain at 60 min, against 30 for XGBoost. 6. It also catches the most lows on my data: 40-47% at 60 min, against 29% for XGBoost. On MetaboNet every low-RMSE model misses nearly all lows XGBoost 0%, T1DMAI 6-8% , while linear extrapolation catches 58%. | Model | Family | Description | |---|---|---| | Persistence | Baseline | Last reading carried forward. | | Linear extrapolation | Baseline | Straight line through the last few readings. | | AR ridge | Classical | Linear regression on recent glucose. | | ARX ridge | Classical | Linear regression on recent glucose, insulin and carbs. | | ARIMA | Classical | Glucose only. | | SARIMAX | Classical | With insulin and carbs. | | Kalman filter | Classical | Damped local linear trend. | | SVR | Classical | Glucose, insulin and carbs. | | Sparse GP | Classical | 1000 inducing points. | | XGBoost | Classical | Glucose, insulin and carbs. | | LSTM | Deep | 2 hours of history Martinsson et al. 2019 . | | GluNet | Deep | Dilated causal convolutions over 4 hours Li et al. 2020 . | | PatchTST | Deep | | | N-HiTS | Deep | | | Chronos-Bolt tiny and mini | Foundation | 9M and 21M parameters, glucose only, zero-shot and fine-tuned. | | T1DMAI D16 DILATE and D16 MSE | T1DMAI | 31k parameters. Glucose, carbs and insulin. | Everything except the baselines and Chronos-Bolt was trained from scratch on each dataset's training split. T1DMAI comes in two versions: sim only is the checkpoint in models/ , and sim + LoRA adds a frozen-base rank 1 adapter 3771 parameters trained on the real training split. T1DMAI details | Architecture | encoder-only transformer, 30 min patches, one attention head, RoPE, SwiGLU | | Size | 16 layers of width 16, 31k parameters | | Loss | 7 quantile pinball loss plus a median term DILATE or MSE | | Inputs | CGM in a risk-scaled space, carb absorption curve, insulin action curve, mask bit | | Output | 7 quantiles 5% to 95% for every 5 minute step of the next 2 hours | | History | up to 7 days | | Simulator training | 1M simulated people, 7 days each, 10000 steps of batch 512 | | Runtime | ExecuTorch or LiteRT, on the phone in T1DMDROID | More in docs/USAGE.md https://github.com/0xdeadf1sh/T1DMAI/blob/main/docs/USAGE.md and ARCHITECTURE.md https://github.com/0xdeadf1sh/T1DMAI/blob/main/ARCHITECTURE.md . My data. 115 days from T1DMDROID: days 1 to 80 for training, 81 to 115 for testing. Note that the model sees future inputs like insulin & carbs. I should probably also train the adapters while withholding those inputs. | Source | Subjects | Person-days | Forecasts | CGM coverage % | Mean mg/dL | CV % | < 54 % | < 70 % | 70-180 % | 180 % | 250 % | |---|---|---|---|---|---|---|---|---|---|---|---| | t1dmdroid | 1 | 35 | 9764 | 97 | 130 | 28 | 0.4 | 3.0 | 89 | 8 | 0 | MetaboNet. Official split, 279 test people from 9 studies. Blind, because most of them use closed-loop pumps whose future insulin reacts to glucose the model hasn't seen. | Source | Subjects | Person-days | Forecasts | CGM coverage % | Mean mg/dL | CV % | < 54 % | < 70 % | 70-180 % | 180 % | 250 % | |---|---|---|---|---|---|---|---|---|---|---|---| | AZT1D | 4 | 131 | 775 | 99 | 141 | 30 | 0.3 | 1.3 | 82 | 17 | 2 | | BrisT1D | 2 | 312 | 1953 | 77 | 144 | 33 | 0.2 | 1.8 | 78 | 20 | 3 | | CTR3 | 5 | 1157 | 1052 | 51 | 141 | 38 | 0.7 | 3.5 | 78 | 19 | 4 | | HUPA-UCM | 4 | 315 | 896 | 100 | 132 | 36 | 0.9 | 6.8 | 78 | 16 | 2 | | IOBP2 | 62 | 4737 | 14653 | 84 | 159 | 37 | 0.4 | 2.0 | 68 | 30 | 8 | | Loop | 159 | 47438 | 203538 | 93 | 139 | 40 | 0.7 | 3.9 | 77 | 19 | 5 | | PEDAP | 6 | 2070 | 6321 | 85 | 158 | 38 | 0.4 | 2.4 | 68 | 29 | 9 | | ReplaceBG | 35 | 7967 | 35683 | 87 | 158 | 41 | 1.0 | 4.2 | 64 | 32 | 9 | | ShanghaiT1DM | 2 | 19 | 28 | 33 | 159 | 39 | 1.0 | 3.8 | 63 | 33 | 7 | | All | 279 | 64146 | 264899 | 90 | 143 | 40 | 0.7 | 3.7 | 75 | 22 | 6 | | | My data | MetaboNet | |---|---|---| | Forecasts | every 5 min of the test days | every 10th row of the leaderboard template | | Horizons | 30, 60, 90, 120 min | 30, 60, 90, 120 min | | Training windows, other models | 60000 | 351000 200000 for fine-tuned Chronos | | LoRA steps | 2000 | 6000 | - LoRA checkpoints are picked by mean RMSE on the last 10 days of each subject's training data. - All models forecast at the same moments. - 95% intervals: bootstrap over people MetaboNet or days my data . - Significance: Wilcoxon signed-rank on paired absolute errors against the best model, Bonferroni corrected. | Model | Parameter count | Microseconds per forecast | History used | |---|---|---|---| | Persistence | 0 | 0.0042 | 5 min | | Linear extrapolation | 0 | 0.033 | 30 min | | AR ridge | 648 | 0.13 | 2 h | | SARIMAX | 8,088 | 0.21 | 8 h | | ARIMA | 4,632 | 0.27 | 4 h | | ARX ridge | 1,116 | 0.36 | 6 h | | Kalman filter | 864 | 0.48 | 3 h | | SVR | 69,772 | 4.2 | 6 h | | Sparse GP | 66,134 | 6.1 | 6 h | | N-HiTS | 417,772 | 6.5 | 6 h | | LSTM | 103,192 | 33 | 2 h | | GluNet | 65,368 | 71 | 4 h | | PatchTST | 182,266 | 74 | 6 h | | XGBoost | 2,692,860 | 506 | 6 h | | Chronos-Bolt tiny fine-tuned | 8,669,901 | 1,346 | 24 h | | Chronos-Bolt tiny zero-shot | 8,669,901 | 1,361 | 24 h | | Chronos-Bolt mini zero-shot | 21,256,141 | 3,871 | 24 h | | Chronos-Bolt mini fine-tuned | 21,256,141 | 3,889 | 24 h | | T1DMAI D16 DILATE | 31,253 | 5,679 | 168 h | | T1DMAI D16 MSE | 31,253 | 7,342 | 168 h | - My data is N=1 over 35 days. - The test sets differ a lot in range 89% vs 75%, CV 28% vs 40% , so don't compare numbers across them. - Lows are rare: 292 forecasts on my data, 8347 on MetaboNet. - T1DMAI fell back to the last reading on 0.7% of my forecasts and 0.4% on MetaboNet, including all 28 ShanghaiT1DM ones. The other models never fall back. - The bands are raw quantiles, no conformal correction. - The sparse GP's own uncertainty isn't shown. Its noise fit hit the bottom of its grid, giving a band about 1 mg/dL wide. - Time gain is only computed for my data. - Unlogged meals count as "no meal". - CPU timings are single runs on one laptop core and varied by up to 45% between runs. | Term | Meaning | |---|---| | Non-blind | The model gets the carbs and insulin logged after the forecast moment. | | Blind | It doesn't. | | Time gain | Horizon minus the lag of peak correlation between forecast and CGM. | | Example forecasts | Median case of: falls into a low, rises over 40 mg/dL after a meal, calm nights. | | After a meal | At least 5 g of carbs logged in the 2 hours before. | | Parameter count | Stored weights and constants in the exported model. | A marks the lowest RMSE and the models not significantly worse than it. My data | Model | Family | RMSE 30 | RMSE 60 | RMSE 90 | RMSE 120 | gRMSE 60 | MARD 30 | MARD 60 | MARD 120 | ME 60 | MRE 60 % | TG 30 | TG 60 | |---|---|---|---|---|---|---|---|---|---|---|---|---|---| | XGBoost | Classical | 14.7 | 22.2 | 26.9 | 31.1 | 24.2 | 8.5 | 13.4 | 19.8 | +0.8 | +3.1 | 15 | 30 | | T1DMAI D16 DILATE sim + LoRA | T1DMAI | 15.2 | 22.8 | 27.4 | 31.7 | 24.7 | 8.8 | 13.5 | 20.3 | -1.6 | +1.2 | 15 | 40 | | SVR | Classical | 15.1 | 23.0 | 27.8 | 31.6 | 24.9 | 8.5 | 13.6 | 20.0 | -0.7 | +1.8 | 10 | 25 | | PatchTST | Deep | 15.1 | 23.3 | 28.9 | 33.1 | 25.0 | 8.9 | 14.3 | 21.8 | +1.3 | +3.3 | 10 | 25 | | Sparse GP | Classical | 15.4 | 23.5 | 28.3 | 32.1 | 25.6 | 9.1 | 14.4 | 21.0 | +0.8 | +3.4 | 10 | 25 | | T1DMAI D16 MSE sim + LoRA | T1DMAI | 15.1 | 23.5 | 28.4 | 32.4 | 25.4 | 8.7 | 13.9 | 20.3 | -0.2 | +2.0 | 20 | 50 | | GluNet | Deep | 15.4 | 23.6 | 29.6 | 34.4 | 25.3 | 8.9 | 14.3 | 22.3 | +1.0 | +2.8 | 10 | 25 | | ARX ridge | Classical | 15.7 | 24.1 | 28.6 | 31.6 | 26.3 | 9.4 | 15.1 | 21.2 | +1.0 | +4.0 | 10 | 15 | | LSTM | Deep | 15.4 | 24.3 | 30.6 | 35.8 | 25.7 | 8.9 | 14.6 | 22.9 | +0.2 | +1.9 | 10 | 20 | | N-HiTS | Deep | 15.8 | 25.0 | 31.4 | 36.0 | 26.6 | 9.5 | 15.5 | 23.3 | +2.0 | +3.5 | 15 | 30 | | T1DMAI D16 DILATE sim only | T1DMAI | 16.2 | 26.1 | 33.2 | 39.9 | 28.6 | 9.6 | 16.0 | 25.2 | +0.6 | +3.9 | 10 | 15 | | T1DMAI D16 MSE sim only | T1DMAI | 16.8 | 27.1 | 33.9 | 37.5 | 29.8 | 9.9 | 16.8 | 24.4 | +1.2 | +4.8 | 10 | 15 | | AR ridge | Classical | 17.2 | 27.3 | 32.3 | 34.5 | 30.3 | 10.3 | 17.2 | 23.0 | -0.1 | +4.1 | 5 | 5 | | Chronos-Bolt tiny fine-tuned | Foundation | 17.3 | 27.3 | 33.0 | 36.0 | 30.0 | 9.8 | 16.4 | 23.4 | -2.2 | +1.7 | 5 | 10 | | Chronos-Bolt mini fine-tuned | Foundation | 17.3 | 27.3 | 32.9 | 36.0 | 29.9 | 9.8 | 16.5 | 23.6 | -1.5 | +2.2 | 5 | 10 | | SARIMAX | Classical | 17.7 | 29.8 | 38.6 | 45.4 | 31.6 | 10.2 | 17.9 | 28.8 | -3.5 | -0.6 | 5 | 5 | | Chronos-Bolt tiny zero-shot | Foundation | 18.7 | 30.8 | 38.3 | 42.0 | 32.9 | 10.8 | 18.5 | 27.1 | +0.8 | +3.5 | 5 | 5 | | Chronos-Bolt mini zero-shot | Foundation | 18.7 | 30.9 | 38.3 | 42.4 | 32.9 | 10.7 | 18.5 | 27.2 | +1.0 | +3.6 | 5 | 10 | | Kalman filter | Classical | 18.5 | 31.3 | 39.6 | 45.0 | 33.2 | 10.6 | 18.7 | 28.6 | +0.2 | +2.7 | 0 | 5 | | ARIMA | Classical | 18.6 | 31.4 | 39.7 | 45.1 | 33.3 | 10.7 | 18.8 | 28.7 | +0.2 | +2.7 | 0 | 5 | | Persistence | Baseline | 20.0 | 32.1 | 39.7 | 44.7 | 34.2 | 11.7 | 19.4 | 28.5 | +0.2 | +3.3 | 0 | 0 | | Linear extrapolation | Baseline | 24.3 | 48.9 | 71.0 | 89.2 | 50.1 | 13.3 | 27.9 | 56.4 | +0.1 | -0.3 | 10 | 15 | | Model | DTS A 30 | DTS A 60 | DTS A 120 | DTS A+B 120 | Clarke A 60 | Clarke A+B 120 | CG-EGA hypo AP 30 | hypo EP 60 | eu EP 60 | hyper EP 60 | |---|---|---|---|---|---|---|---|---|---|---| | XGBoost | 91.5 | 78.3 | 62.8 | 96.9 | 78.6 | 96.6 | 77.7 | 51.9 | 0.0 | 0.1 | | T1DMAI D16 DILATE sim + LoRA | 90.7 | 78.0 | 60.7 | 96.8 | 78.3 | 97.0 | 83.2 | 38.4 | 0.0 | 0.1 | | SVR | 91.3 | 77.5 | 61.3 | 97.3 | 77.8 | 97.1 | 83.5 | 47.4 | 0.0 | 0.8 | | PatchTST | 90.9 | 75.0 | 57.6 | 96.5 | 75.3 | 96.9 | 82.3 | 48.1 | 0.0 | 0.4 | | Sparse GP | 89.9 | 75.7 | 59.2 | 97.1 | 76.1 | 96.8 | 71.9 | 58.7 | 0.0 | 0.8 | | T1DMAI D16 MSE sim + LoRA | 91.2 | 76.4 | 60.0 | 96.7 | 76.7 | 97.0 | 88.1 | 35.5 | 0.0 | 0.3 | | GluNet | 90.7 | 75.8 | 56.3 | 96.0 | 76.1 | 96.8 | 86.8 | 42.3 | 0.0 | 0.1 | | ARX ridge | 90.0 | 73.7 | 59.5 | 96.7 | 74.0 | 96.7 | 69.7 | 72.3 | 0.0 | 0.1 | | LSTM | 90.8 | 75.6 | 55.3 | 95.5 | 75.9 | 97.1 | 89.4 | 31.6 | 0.0 | 0.1 | | N-HiTS | 89.8 | 72.4 | 54.5 | 95.4 | 72.8 | 97.1 | 84.2 | 33.9 | 0.0 | 0.1 | | T1DMAI D16 DILATE sim only | 89.0 | 72.1 | 50.8 | 93.0 | 72.4 | 95.8 | 73.5 | 62.6 | 0.1 | 0.4 | | T1DMAI D16 MSE sim only | 88.2 | 69.7 | 54.0 | 93.9 | 70.1 | 96.2 | 69.4 | 71.9 | 0.1 | 0.5 | | AR ridge | 87.1 | 67.7 | 55.7 | 95.9 | 67.9 | 96.5 | 53.2 | 97.7 | 0.0 | 0.3 | | Chronos-Bolt tiny fine-tuned | 88.3 | 69.8 | 54.1 | 95.8 | 70.0 | 96.4 | 71.6 | 76.5 | 0.0 | 0.3 | | Chronos-Bolt mini fine-tuned | 88.2 | 69.5 | 54.0 | 95.6 | 69.7 | 96.4 | 73.9 | 80.6 | 0.0 | 0.4 | | SARIMAX | 87.6 | 64.7 | 43.5 | 89.8 | 65.2 | 97.4 | 90.6 | 27.4 | 0.0 | 0.3 | | Chronos-Bolt tiny zero-shot | 86.0 | 64.4 | 49.5 | 92.4 | 64.7 | 95.8 | 79.4 | 53.9 | 0.0 | 0.3 | | Chronos-Bolt mini zero-shot | 86.2 | 64.6 | 49.9 | 92.3 | 64.9 | 95.6 | 82.6 | 54.5 | 0.0 | 0.3 | | Kalman filter | 86.1 | 64.0 | 47.1 | 90.8 | 64.3 | 96.1 | 85.2 | 46.8 | 0.1 | 0.3 | | ARIMA | 86.0 | 64.0 | 46.9 | 90.8 | 64.3 | 96.0 | 85.2 | 46.8 | 0.1 | 0.3 | | Persistence | 82.9 | 62.5 | 47.5 | 91.0 | 63.1 | 96.1 | 79.7 | 51.3 | 0.0 | 0.3 | | Linear extrapolation | 78.7 | 50.5 | 25.9 | 65.9 | 51.3 | 83.2 | 91.9 | 23.2 | 2.0 | 2.6 | | Model | Sens 30 | Sens 60 | Spec 60 | PPV 60 | MCC 30 | MCC 60 | RMSE<70 30 | ME<70 30 | ME<70 60 | |---|---|---|---|---|---|---|---|---|---| | XGBoost | 0.49 | 0.29 | 0.997 | 0.73 | 0.62 | 0.45 | 13.1 | +9.1 | +17.6 | | T1DMAI D16 DILATE sim + LoRA | 0.63 | 0.40 | 0.993 | 0.65 | 0.69 | 0.50 | 11.3 | +6.2 | +13.0 | | SVR | 0.62 | 0.34 | 0.992 | 0.58 | 0.66 | 0.43 | 10.7 | +6.2 | +14.4 | | PatchTST | 0.57 | 0.39 | 0.989 | 0.52 | 0.63 | 0.43 | 11.7 | +4.9 | +11.7 | | Sparse GP | 0.51 | 0.23 | 0.995 | 0.57 | 0.60 | 0.35 | 13.7 | +9.5 | +19.0 | | T1DMAI D16 MSE sim + LoRA | 0.66 | 0.47 | 0.992 | 0.64 | 0.70 | 0.54 | 10.3 | +4.7 | +10.9 | | GluNet | 0.66 | 0.49 | 0.987 | 0.55 | 0.66 | 0.50 | 11.1 | +3.3 | +8.5 | | ARX ridge | 0.38 | 0.18 | 0.995 | 0.54 | 0.49 | 0.30 | 13.4 | +10.1 | +21.5 | | LSTM | 0.70 | 0.53 | 0.983 | 0.50 | 0.65 | 0.50 | 10.3 | +1.5 | +5.2 | | N-HiTS | 0.65 | 0.53 | 0.984 | 0.50 | 0.66 | 0.50 | 12.4 | +3.0 | +7.8 | | T1DMAI D16 DILATE sim only | 0.51 | 0.21 | 0.997 | 0.69 | 0.64 | 0.37 | 13.2 | +9.2 | +20.2 | | T1DMAI D16 MSE sim only | 0.42 | 0.14 | 0.997 | 0.62 | 0.58 | 0.28 | 14.1 | +10.5 | +22.3 | | AR ridge | 0.22 | 0.00 | 0.999 | 0.00 | 0.33 | -0.00 | 16.1 | +14.3 | +31.4 | | Chronos-Bolt tiny fine-tuned | 0.43 | 0.07 | 0.993 | 0.24 | 0.48 | 0.11 | 13.4 | +10.1 | +23.4 | | Chronos-Bolt mini fine-tuned | 0.46 | 0.06 | 0.994 | 0.23 | 0.51 | 0.11 | 12.9 | +9.5 | +24.4 | | SARIMAX | 0.71 | 0.55 | 0.964 | 0.32 | 0.60 | 0.40 | 9.2 | +2.3 | +7.6 | | Chronos-Bolt tiny zero-shot | 0.55 | 0.34 | 0.980 | 0.34 | 0.51 | 0.32 | 12.3 | +6.4 | +17.8 | | Chronos-Bolt mini zero-shot | 0.60 | 0.29 | 0.982 | 0.34 | 0.54 | 0.30 | 11.9 | +5.8 | +18.5 | | Kalman filter | 0.63 | 0.40 | 0.974 | 0.33 | 0.56 | 0.34 | 10.7 | +5.6 | +15.0 | | ARIMA | 0.63 | 0.40 | 0.974 | 0.33 | 0.56 | 0.34 | 10.7 | +5.7 | +14.9 | | Persistence | 0.55 | 0.36 | 0.981 | 0.36 | 0.54 | 0.34 | 12.8 | +8.1 | +18.1 | | Linear extrapolation | 0.79 | 0.72 | 0.872 | 0.15 | 0.47 | 0.29 | 11.1 | -1.8 | -3.5 | 9661 forecasts at 60 min. means lowest RMSE, or not significantly worse than it Wilcoxon test on absolute errors, alpha 0.05/21 . MetaboNet | Model | Family | RMSE 30 | RMSE 60 | RMSE 90 | RMSE 120 | gRMSE 60 | MARD 30 | MARD 60 | MARD 120 | ME 60 | MRE 60 % | |---|---|---|---|---|---|---|---|---|---|---|---| | XGBoost | Classical | 19.8 | 33.2 | 41.3 | 46.0 | 40.3 | 10.6 | 18.6 | 27.2 | +0.5 | +6.3 | | SVR | Classical | 20.4 | 33.9 | 41.9 | 46.6 | 40.9 | 10.5 | 18.1 | 25.5 | -2.5 | +3.5 | | Sparse GP | Classical | 20.8 | 34.3 | 42.1 | 46.6 | 41.6 | 11.1 | 19.1 | 27.5 | +0.3 | +6.3 | | N-HiTS | Deep | 20.6 | 34.4 | 42.7 | 47.8 | 40.8 | 11.0 | 19.0 | 27.4 | +0.5 | +5.4 | | PatchTST | Deep | 20.3 | 34.4 | 42.9 | 48.0 | 40.7 | 10.7 | 19.0 | 27.7 | +0.7 | +5.5 | | Chronos-Bolt mini fine-tuned | Foundation | 21.1 | 34.5 | 42.0 | 46.2 | 40.9 | 10.8 | 18.1 | 25.0 | -2.6 | +2.6 | | T1DMAI D16 DILATE sim + LoRA | T1DMAI | 21.5 | 34.6 | 41.0 | 50.4 | 41.2 | 10.9 | 18.0 | 25.2 | -3.5 | +2.0 | | Chronos-Bolt tiny fine-tuned | Foundation | 21.2 | 34.6 | 42.2 | 46.3 | 41.0 | 10.9 | 18.2 | 25.0 | -2.4 | +2.8 | | GluNet | Deep | 20.6 | 34.8 | 43.6 | 49.0 | 41.1 | 10.8 | 19.0 | 27.7 | +0.2 | +4.9 | | T1DMAI D16 MSE sim + LoRA | T1DMAI | 21.8 | 35.1 | 41.0 | 46.8 | 41.3 | 11.2 | 18.1 | 25.1 | -3.8 | +1.3 | | ARX ridge | Classical | 21.3 | 35.4 | 43.5 | 47.9 | 42.3 | 11.7 | 20.5 | 29.5 | +2.6 | +8.1 | | AR ridge | Classical | 21.4 | 35.6 | 43.6 | 48.1 | 42.4 | 11.7 | 20.7 | 29.9 | +3.3 | +8.5 | | LSTM | Deep | 21.1 | 35.9 | 45.1 | 51.0 | 41.9 | 11.1 | 19.5 | 28.5 | +0.6 | +4.8 | | T1DMAI D16 DILATE sim only | T1DMAI | 23.4 | 36.6 | 44.0 | 48.0 | 44.0 | 12.2 | 20.4 | 28.8 | +0.0 | +6.0 | | T1DMAI D16 MSE sim only | T1DMAI | 23.4 | 36.7 | 44.1 | 48.2 | 43.8 | 13.1 | 21.7 | 29.7 | +4.4 | +9.7 | | Chronos-Bolt tiny zero-shot | Foundation | 22.8 | 37.7 | 46.4 | 51.3 | 43.3 | 11.9 | 20.1 | 28.3 | +0.7 | +4.4 | | Chronos-Bolt mini zero-shot | Foundation | 22.9 | 37.7 | 46.3 | 51.4 | 43.4 | 11.8 | 19.9 | 28.2 | +0.1 | +3.8 | | Kalman filter | Classical | 22.2 | 38.4 | 48.9 | 55.6 | 43.9 | 11.4 | 20.3 | 30.3 | +0.2 | +3.5 | | SARIMAX | Classical | 22.3 | 39.2 | 51.0 | 59.4 | 44.3 | 11.6 | 21.2 | 33.7 | +2.2 | +4.8 | | Persistence | Baseline | 25.2 | 40.0 | 49.4 | 55.5 | 46.2 | 13.1 | 21.3 | 30.3 | +0.3 | +4.4 | | ARIMA | Classical | 23.4 | 41.9 | 55.1 | 64.6 | 47.1 | 12.1 | 22.2 | 35.5 | +0.1 | +2.6 | | Linear extrapolation | Baseline | 30.7 | 59.2 | 81.8 | 98.8 | 64.0 | 15.6 | 31.4 | 56.8 | +0.1 | +0.7 | | Model | DTS A 30 | DTS A 60 | DTS A 120 | DTS A+B 120 | Clarke A 60 | Clarke A+B 120 | CG-EGA hypo AP 30 | hypo EP 60 | eu EP 60 | hyper EP 60 | |---|---|---|---|---|---|---|---|---|---|---| | XGBoost | 86.0 | 66.9 | 49.6 | 93.0 | 67.0 | 93.1 | 33.5 | 99.4 | 0.1 | 3.1 | | SVR | 86.0 | 67.6 | 51.6 | 93.6 | 67.8 | 92.9 | 61.7 | 91.3 | 0.1 | 3.4 | | Sparse GP | 84.8 | 66.0 | 49.3 | 92.7 | 66.2 | 93.1 | 45.8 | 97.1 | 0.1 | 3.0 | | N-HiTS | 85.0 | 66.1 | 49.1 | 92.4 | 66.3 | 93.3 | 57.5 | 80.0 | 0.1 | 2.9 | | PatchTST | 85.7 | 66.0 | 48.8 | 92.3 | 66.2 | 93.3 | 63.9 | 79.7 | 0.1 | 2.7 | | Chronos-Bolt mini fine-tuned | 85.4 | 67.7 | 53.1 | 93.7 | 67.9 | 93.3 | 65.6 | 77.4 | 0.1 | 3.1 | | T1DMAI D16 DILATE sim + LoRA | 84.9 | 67.8 | 49.5 | 93.1 | 68.0 | 91.8 | 71.7 | 79.8 | 0.1 | 3.2 | | Chronos-Bolt tiny fine-tuned | 85.2 | 67.5 | 53.0 | 93.7 | 67.7 | 93.3 | 62.2 | 77.8 | 0.1 | 3.1 | | GluNet | 85.4 | 66.0 | 48.7 | 91.9 | 66.2 | 93.1 | 64.2 | 75.9 | 0.1 | 2.8 | | T1DMAI D16 MSE sim + LoRA | 84.4 | 67.7 | 51.5 | 94.0 | 67.9 | 92.4 | 68.5 | 74.8 | 0.1 | 2.9 | | ARX ridge | 83.8 | 62.0 | 45.3 | 91.9 | 62.2 | 93.2 | 51.1 | 93.4 | 0.1 | 2.8 | | AR ridge | 83.7 | 61.8 | 45.1 | 91.7 | 61.9 | 93.2 | 52.1 | 93.5 | 0.1 | 2.8 | | LSTM | 84.9 | 64.9 | 47.7 | 91.1 | 65.1 | 92.8 | 67.5 | 72.4 | 0.1 | 2.8 | | T1DMAI D16 DILATE sim only | 82.3 | 62.4 | 45.3 | 92.4 | 62.6 | 92.7 | 56.0 | 86.3 | 0.1 | 3.3 | | T1DMAI D16 MSE sim only | 80.1 | 59.2 | 45.0 | 91.8 | 59.3 | 93.3 | 34.6 | 93.9 | 0.1 | 3.1 | | Chronos-Bolt tiny zero-shot | 82.8 | 63.9 | 49.2 | 90.9 | 64.1 | 92.8 | 66.9 | 67.9 | 0.3 | 3.0 | | Chronos-Bolt mini zero-shot | 83.1 | 64.2 | 49.4 | 91.0 | 64.4 | 92.7 | 70.7 | 66.9 | 0.3 | 3.1 | | Kalman filter | 83.9 | 63.3 | 46.3 | 88.5 | 63.6 | 92.2 | 74.7 | 61.0 | 0.3 | 2.9 | | SARIMAX | 83.6 | 61.5 | 42.0 | 86.5 | 61.8 | 91.1 | 74.2 | 61.2 | 0.4 | 2.7 | | Persistence | 79.6 | 61.0 | 46.1 | 88.7 | 61.5 | 92.1 | 57.3 | 68.3 | 0.2 | 3.4 | | ARIMA | 82.4 | 59.3 | 40.1 | 83.6 | 59.7 | 89.8 | 78.0 | 51.9 | 0.5 | 3.3 | | Linear extrapolation | 74.9 | 48.6 | 26.8 | 67.0 | 49.4 | 78.0 | 83.5 | 34.3 | 3.6 | 7.8 | | Model | Sens 30 | Sens 60 | Spec 60 | PPV 60 | MCC 30 | MCC 60 | RMSE<70 30 | ME<70 30 | ME<70 60 | |---|---|---|---|---|---|---|---|---|---| | XGBoost | 0.06 | 0.00 | 1.000 | 0.74 | 0.20 | 0.03 | 26.5 | +22.3 | +45.6 | | SVR | 0.34 | 0.02 | 0.999 | 0.33 | 0.38 | 0.08 | 22.6 | +15.8 | +38.4 | | Sparse GP | 0.22 | 0.01 | 0.999 | 0.27 | 0.31 | 0.04 | 25.9 | +19.9 | +44.5 | | N-HiTS | 0.34 | 0.10 | 0.992 | 0.29 | 0.39 | 0.16 | 24.3 | +16.3 | +37.0 | | PatchTST | 0.40 | 0.10 | 0.992 | 0.28 | 0.42 | 0.15 | 22.8 | +14.4 | +36.0 | | Chronos-Bolt mini fine-tuned | 0.40 | 0.10 | 0.993 | 0.32 | 0.41 | 0.16 | 21.9 | +14.1 | +33.5 | | T1DMAI D16 DILATE sim + LoRA | 0.42 | 0.06 | 0.997 | 0.38 | 0.41 | 0.15 | 20.6 | +13.3 | +33.4 | | Chronos-Bolt tiny fine-tuned | 0.37 | 0.10 | 0.993 | 0.30 | 0.40 | 0.16 | 22.5 | +15.0 | +33.7 | | GluNet | 0.41 | 0.13 | 0.988 | 0.26 | 0.42 | 0.17 | 22.9 | +14.1 | +34.8 | | T1DMAI D16 MSE sim + LoRA | 0.40 | 0.08 | 0.996 | 0.37 | 0.40 | 0.16 | 21.7 | +14.2 | +32.1 | | ARX ridge | 0.27 | 0.03 | 0.996 | 0.20 | 0.33 | 0.07 | 24.5 | +17.7 | +42.7 | | AR ridge | 0.28 | 0.03 | 0.995 | 0.19 | 0.33 | 0.07 | 24.0 | +17.3 | +42.9 | | LSTM | 0.44 | 0.16 | 0.982 | 0.23 | 0.42 | 0.17 | 22.7 | +13.0 | +33.2 | | T1DMAI D16 DILATE sim only | 0.32 | 0.07 | 0.991 | 0.20 | 0.34 | 0.10 | 24.9 | +16.9 | +39.3 | | T1DMAI D16 MSE sim only | 0.18 | 0.03 | 0.997 | 0.22 | 0.27 | 0.07 | 28.6 | +22.5 | +45.1 | | Chronos-Bolt tiny zero-shot | 0.46 | 0.20 | 0.976 | 0.21 | 0.41 | 0.18 | 23.2 | +12.7 | +31.9 | | Chronos-Bolt mini zero-shot | 0.49 | 0.20 | 0.976 | 0.21 | 0.41 | 0.18 | 22.4 | +11.5 | +31.0 | | Kalman filter | 0.52 | 0.26 | 0.963 | 0.19 | 0.43 | 0.19 | 20.7 | +9.8 | +28.3 | | SARIMAX | 0.53 | 0.27 | 0.961 | 0.18 | 0.43 | 0.19 | 21.2 | +9.8 | +28.8 | | Persistence | 0.37 | 0.19 | 0.975 | 0.19 | 0.35 | 0.16 | 26.1 | +16.3 | +33.6 | | ARIMA | 0.59 | 0.37 | 0.938 | 0.16 | 0.43 | 0.21 | 20.7 | +7.3 | +23.8 | | Linear extrapolation | 0.72 | 0.58 | 0.861 | 0.12 | 0.41 | 0.21 | 27.2 | -1.4 | +12.3 | 264893 forecasts at 60 min. means lowest RMSE, or not significantly worse than it Wilcoxon test on absolute errors, alpha 0.05/21 . Raw numbers are in results/ https://github.com/0xdeadf1sh/T1DMAI/blob/main/results . python -m zoo.run MODEL --cache CACHE --template template.parquet --truth truth.parquet --causal python finetune.py --checkpoint CKPT --cache CACHE --lora-rank 1 --val-days 10 --select-on rmse \ --blind | --no-blind python export submission.py --checkpoint CKPT --template template.parquet --cache CACHE \ --max-context 336 --fill ladder --quantiles --future-doses zero python leaderboard.py MANIFEST.json --out results Copyright 2026 0xdeadf1sh. MIT https://github.com/0xdeadf1sh/T1DMAI/blob/main/LICENSE . See also T1DMSIM https://github.com/0xdeadf1sh/T1DMSIM , T1DMDROID https://github.com/0xdeadf1sh/T1DMDROID and T1DMCOMMON https://github.com/0xdeadf1sh/T1DMCOMMON .