NaiveForecaster

class omnicast.NaiveForecaster[source]

Bases: BaseForecaster

Random-walk forecast using the most recent observation.

Examples

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

Notes

When to use this model

Best for

The floor baseline every other model must beat; no assumptions about the series beyond “tomorrow looks like today”

Avoid when

The series has a visible trend or seasonal cycle – it will systematically lag both

Handles trend

No

Handles seasonality

No

Extra dependencies

None

Min. observations

1
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:

The simplest possible forecast: repeat the last observed value, with horizon-scaled Gaussian intervals (sqrt(sigma2 * h), i.e. random-walk variance growth). Use it as the floor every other model must beat – if a fancier model can’t out-backtest NaiveForecaster, it isn’t earning its complexity.

from omnicast import NaiveForecaster

# y is the sample series defined on the model guide's index page
model = NaiveForecaster().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  197.6    190.74    204.46    187.11    208.09
2026-02  197.6    187.90    207.30    182.76    212.44
2026-03  197.6    185.72    209.48    179.43    215.77
2026-04  197.6    183.88    211.32    176.61    218.59
2026-05  197.6    182.26    212.94    174.14    221.06
2026-06  197.6    180.79    214.41    171.90    223.30

The point forecast is flat at the last observed value (197.6), and the interval widens with the square root of the horizon – exactly as random-walk theory predicts. fitted_values_[0] is NaN because there is no prior observation to predict the first point from:

model.fitted_values_.head(2)
# 2022-01      NaN
# 2022-02    100.0