60-second start
Quickstart
One install, one trained model, three kinds of questions. Continuous-time forecasting with calibrated uncertainty on both value and time.
$
pip install apdtflow The whole API at a glance
Two flags unlock the timing capabilities:
decoder_type='continuous' swaps in the
Neural-ODE decoder you can query at any real-valued time, and
use_conformal=True calibrates the
uncertainty windows.
python
from apdtflow import APDTFlowForecaster
model = APDTFlowForecaster(forecast_horizon=40, decoder_type='continuous',
use_conformal=True)
model.fit(df, target_col='capacity', feature_cols=sensor_cols)
model.predict() # classic grid forecast
model.predict_at(['2026-06-14 14:37', 3.6]) # value at ANY moment
result = model.predict_when(threshold=1.4, # WHEN it crosses the line
direction='below')
result.eta, result.act_by, result.censored
schedule = model.predict_when_fleet(assets, # whole fleet -> ranked schedule
threshold=1.4, direction='below')predict() Next k values on the grid, with optional conformal intervals.
predict_at(times) Value at arbitrary timestamps — fractional steps, beyond the horizon.
predict_when(threshold) A calibrated 90% window on the crossing time, plus an act_by edge.
Prefer a runnable notebook?
The Quickstart notebook covers all three questions end-to-end and opens in Colab with zero setup.