See a forecast move.

Real output from aadya-m1, precomputed offline from the released weights, so this page needs no server and sends nothing anywhere.

Pick a cycle history

Logged cycle lengths: 26, 31, 28, 33, 27, 30 days · age group 25-34

Days since the last period started: 0

Watch the forecast update as time passes without a period. This is the same model, asked again each day.

Optional, experimental: an ovulation test (the surge had passed by day 17)

Studied on one small study of 41 people. Never an ovulation date.

If the real cycle turns out to be 29 days long, the error score (CRPS, lower is better) is 0.70. A flat 28-day guess would score 0.71.

80% window for these inputs: days 25 to 33. Real output of aadya-m1, precomputed offline from the released weights; nothing is faked and nothing is sent anywhere.

To forecast from your own cycle data, run the model on your own machine so the data never leaves it (below). The hosted demo shows the same precomputed examples.

On your machine

Run it yourself.

# Python 3.10+. Weights are downloaded once, then everything runs offline.
pip install numpy scipy torch safetensors huggingface_hub