- pytorch_forecasting.data.examples.generate_ar_data(n_series: int = 10, timesteps: int = 400, seasonality: float = 3.0, trend: float = 3.0, noise: float = 0.1, level: float = 1.0, exp: bool = False, seed: int = 213) DataFrame #
Generate multivariate data without covariates.
Eeach timeseries is generated from seasonality and trend. Important columns:
series: series ID
time_idx: time index
value: target value
n_series (int, optional) – Number of series. Defaults to 10.
timesteps (int, optional) – Number of timesteps. Defaults to 400.
seasonality (float, optional) – Normalized frequency, i.e. frequency is
seasonality / timesteps. Defaults to 3.0.
trend (float, optional) – Trend multiplier (seasonality is multiplied with 1.0). Defaults to 3.0.
noise (float, optional) – Level of gaussian noise. Defaults to 0.1.
level (float, optional) – Level multiplier (level is a constant to be aded to timeseries). Defaults to 1.0.
exp (bool, optional) – If to return exponential of timeseries values. Defaults to False.
seed (int, optional) – Random seed. Defaults to 213.
- Return type: