DriftForecaster

class omnicast.DriftForecaster[source]

Bases: BaseForecaster

Random walk with drift between the first and last observations.

Examples

>>> import pandas as pd
>>> from omnicast import DriftForecaster
>>> y = pd.Series([10.0, 12.0, 11.0, 13.0, 15.0, 14.0])
>>> model = DriftForecaster().fit(y)
>>> model.predict(horizon=2).mean.round(2).tolist()
[14.8, 15.6]

Notes

When to use this model

Best for

A trending series with no seasonality; a much stronger baseline than NaiveForecaster in that case, at the same cost

Avoid when

The series has seasonality a straight line can’t capture, or the trend is not roughly linear end-to-end

Handles trend

Yes (linear, extrapolated from the first and last observation)

Handles seasonality

No

Extra dependencies

None

Min. observations

2
fit(y, X=None)
Return type:

BaseForecaster

Parameters:
fit_predict(y, horizon, **kwargs)
Return type:

ForecastResult

Parameters:
get_params()
Return type:

dict[str, object]

predict(horizon, X=None, level=(80, 95))
Return type:

ForecastResult

Parameters:

A random walk with drift: extrapolates the straight line between the first and last observation. Cheap, closed-form, and a much stronger baseline than NaiveForecaster on a trending series with no seasonality.

from omnicast import DriftForecaster

model = DriftForecaster().fit(y)
forecast = model.predict(horizon=6, level=[80, 95])
print(forecast.to_frame())

           mean  lower_80  upper_80  lower_95  upper_95
2026-01  199.68    193.29    206.07    189.90    209.45
2026-02  201.75    192.62    210.89    187.79    215.72
2026-03  203.83    192.53    215.13    186.55    221.11
2026-04  205.91    192.73    219.08    185.76    226.06
2026-05  207.98    193.11    222.86    185.23    230.73
2026-06  210.06    193.61    226.51    184.90    235.22

drift_ is (y[-1] - y[0]) / (n - 1) – the average per-step change over the whole series – added to the last observation at each step:

model.drift_   # 2.0766... (~2.08 units/month, close to the synthetic trend of 2.2)

Because this series also has yearly seasonality that a straight line can’t capture, DriftForecaster will systematically over- or under-shoot depending on where in the cycle the forecast horizon falls; compare against ThetaForecaster or ETSForecaster when seasonality matters. fit raises ValueError on a series shorter than two observations, since drift is undefined otherwise.