`ForecasterAutoreg` fails to fit when index do not start from 0 about skforecast HOT 3 CLOSED

Hello @yarnabrina

Thank you for opening the issue!

We have identified the root of the problem. What I can recommend you to avoid this error is to use a pd.RangeIndex object instead of a np.arange(). This will allow you to use an index that doesn't start at 0.

import numpy as np
import pandas as pd

np.random.seed(seed=0)

data = pd.DataFrame(
    np.random.random(size=3 * 20).reshape((20, 3)),
    index=pd.RangeIndex(3, 3 + 20), # Changed to pd.RangeIndex()
    columns=["y", "x1", "x2"],
)

from sklearn.linear_model import LinearRegression
regressor = LinearRegression()

from skforecast.ForecasterAutoreg import ForecasterAutoreg
forecaster = ForecasterAutoreg(regressor, 1)

forecaster.fit(data.iloc[:, 0], exog=data.iloc[:, 1:])

We will have a look 😄

Thank you!

from skforecast.

Hi @yarnabrina

This bug is fixed in version 0.11.0. Although this version is not yet released in PyPI, you can test it by installing skforecast from GitHub.

pip install git+https://github.com/JoaquinAmatRodrigo/[email protected]

Hope it helps!

from skforecast.

Skforecast 0.11.0 has been released in PyPI.

from skforecast.