etna.datasets.generate_ar_df

generate_ar_df(periods: int, start_time: ~pandas._libs.tslibs.timestamps.Timestamp | int | str | None = None, ar_coef: list | None = None, sigma: float = 1, n_segments: int = 1, freq: ~pandas._libs.tslibs.offsets.DateOffset | str | None = <Day>, random_seed: int = 1) → DataFrame

Create DataFrame with AR process data.

Parameters:

• periods (int) – number of timestamps

• start_time (Timestamp | int | str | None) – start timestamp

• ar_coef (list | None) – AR coefficients

• sigma (float) – scale of AR noise

• n_segments (int) – number of segments

• freq (DateOffset | str | None) –

  frequency of timestamps, possible values:

  • pandas.DateOffset object for datetime timestamp

  • pandas offset aliases for datetime timestamp

  • None for integer timestamp

• random_seed (int) – random seed

Raises:

ValueError: – Incorrect type of start_time is used according to freq

Return type:

DataFrame