Open source · PyPI
Dependence-Forecastability
A deterministic pre-modeling layer that asks whether a time series contains exploitable structure — before you spend weeks on model search.
- Python
- Information Theory
- AMI / pAMI
- Transfer Entropy
Most forecasting projects start too late: data → model search → tuning → more features → more compute → still poor results. Dependence-Forecastability inserts the question that should come first:
Does this series contain exploitable structure, and what should a model be allowed to use?
It is not another forecasting library. It's a deterministic pre-modeling layer that inspects readiness, informative horizons, target lags, seasonality structure, covariate usefulness, and leakage risk — before you reach for Darts, MLForecast, StatsForecast, Nixtla, Prophet, or a custom model.
What it does
- Horizon-wise forecastability using interchangeable dependence scorers (AMI / pAMI and KSG-II estimators).
- True Schreiber transfer entropy for covariate screening — which external signals actually carry predictive information, and at which lag.
- Statistical rigor by default — Romano–Wolf FWER correction across lags so you don't fool yourself with multiple-comparison noise.
- Rolling-origin benchmarking and reproducible reporting, with a typed
agent boundary and a physical
domain/layer.
Why it matters
It turns the most expensive guesswork in a forecasting engagement — is this even learnable? — into a fast, defensible, reproducible diagnostic. The companion examples repo walks through it on real datasets.