forecose 0.2.1

A time-series forecasting extension for pydexcom using Google's TimesFM

forecose

A time-series forecasting extension for pydexcom using Google's TimesFM. Used to predict immediate, short term blood glucose readings.

All modelling and forecasting is performed locally on your device. The only external connections made are with:

• Dexcom Share API: fetching CGM readings following the pydexcom approach.
• HuggingFace: one-time download of the forecasting model weights on the first run.

Quick Start

1. Ensure that you have installed the pydexcom package and enabled the Share service within your Dexcom G7 / G6 / G5 / G4.

pip install pydexcom

2. Initialise pydexcom with your Dexcom credentials (below shows the simplist route, refere to pydexcom for further instruction).

>>> from pydexcom import Dexcom
>>> dexcom = Dexcom(username="username", password="password")

3. Generate a prediction.

>>> from forecose import DexcomForecast
>>> predictions = DexcomForecast().get_forecast(dexcom)
>>> print(predictions)
                          timestamp  predicted_glucose         q10         q25         q50         q75         q90
0  2026-06-26 11:41:22.163000+01:00         123.036758  122.259880  114.010910  121.040176  126.698639  130.737625
1  2026-06-26 11:46:22.163000+01:00         117.917084  119.255325  103.586609  114.847458  124.962013  130.337677
2  2026-06-26 11:51:22.163000+01:00         112.936745  114.285713   93.414429  108.460175  122.032082  130.147263
3  2026-06-26 11:56:22.163000+01:00         108.864471  111.334473   85.159195  103.118362  120.449158  129.664490
4  2026-06-26 12:01:22.163000+01:00         105.268646  110.067360   77.075996   98.538086  119.166481  129.867401
5  2026-06-26 12:06:22.163000+01:00         102.510818  108.663223   71.013176   94.973129  118.314079  130.784134
6  2026-06-26 12:11:22.163000+01:00         100.653397  106.934074   66.480339   92.466553  119.067230  132.150269
7  2026-06-26 12:16:22.163000+01:00         100.558281  107.133316   64.823494   91.983261  119.827232  135.091919
8  2026-06-26 12:21:22.163000+01:00         100.579796  109.257736   61.783752   91.251221  122.283157  138.308975
9  2026-06-26 12:26:22.163000+01:00         100.584198  108.955002   59.392654   90.392227  124.204987  140.372147
10 2026-06-26 12:31:22.163000+01:00         100.988571  111.358139   57.296143   89.819542  125.757668  143.972672
11 2026-06-26 12:36:22.163000+01:00         101.895081  112.446609   58.076065   89.992996  128.253937  146.511047

>>> print(predictions.mmol_l)
                          timestamp  predicted_glucose  q10  q25  q50  q75  q90
0  2026-06-26 11:41:22.163000+01:00                6.8  6.8  6.3  6.7  7.0  7.3
1  2026-06-26 11:46:22.163000+01:00                6.5  6.6  5.7  6.4  6.9  7.2
2  2026-06-26 11:51:22.163000+01:00                6.3  6.3  5.2  6.0  6.8  7.2
3  2026-06-26 11:56:22.163000+01:00                6.0  6.2  4.7  5.7  6.7  7.2
4  2026-06-26 12:01:22.163000+01:00                5.8  6.1  4.3  5.5  6.6  7.2
5  2026-06-26 12:06:22.163000+01:00                5.7  6.0  3.9  5.3  6.6  7.3
6  2026-06-26 12:11:22.163000+01:00                5.6  5.9  3.7  5.1  6.6  7.3
7  2026-06-26 12:16:22.163000+01:00                5.6  5.9  3.6  5.1  6.7  7.5
8  2026-06-26 12:21:22.163000+01:00                5.6  6.1  3.4  5.1  6.8  7.7
9  2026-06-26 12:26:22.163000+01:00                5.6  6.0  3.3  5.0  6.9  7.8
10 2026-06-26 12:31:22.163000+01:00                5.6  6.2  3.2  5.0  7.0  8.0
11 2026-06-26 12:36:22.163000+01:00                5.7  6.2  3.2  5.0  7.1  8.1

What do these predictions mean?

forecose applies the TimesFM PyTorch model to blood glucose values retrieved from the pydexcom Python API interface for Dexcom. The predicted_glucose details a point prediction from the resulting probabilistic distribution over the next hour (12x 5 minute interval readings).

The probability quantiles (from q10 to q90) highlights the prediction confidence band and boundaries for the immediate upcoming glucose readings.