forecose 0.1.1

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

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
>>> forecaster = DexcomForecast.from_dexcom( 
        dexcom=dexcom,      # pull recent readings from your active 'Dexcom' session
        context_len=288,    # uses prior day's readings as context
        horizon=12          # predicts the next hour
    )
>>> predictions = forecaster.forecast()
>>> print(predictions.head())
                         timestamp  predicted_glucose       q10       q25       q50       q75       q90
0 2026-06-25 11:01:19.199000+01:00           8.243370  8.244899  8.125346  8.214749  8.266071  8.309934
1 2026-06-25 11:06:19.199000+01:00           8.050682  8.073329  7.738788  8.018662  8.208736  8.303193
2 2026-06-25 11:11:19.199000+01:00           7.897943  7.879324  7.332723  7.783697  8.082028  8.256586
3 2026-06-25 11:16:19.199000+01:00           7.767045  7.738261  6.965607  7.621467  8.026337  8.236394
4 2026-06-25 11:21:19.199000+01:00           7.615633  7.667328  6.668064  7.442780  7.972524  8.216294

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.