Synthetic 🧬 Forecast

pip install synforecast

from synforecast import generate_series

synthetic_df = generate_series(
    n_series=100,
    freq="D",
    min_length=100,
    max_length=100,
    seed=42,
)

from synforecast.generators import SeasonalGenerator

generator = SeasonalGenerator(
    min_length=365,
    max_length=365,
    freq="D",
    seasonality_period=7,
    seasonality_amplitude=10.0,
    seed=42,
)
synthetic_df = generator.generate(n_series=20)

from synforecast import SynAugment

augmenter = SynAugment(seed=42)
augmented_train_df = augmenter.augment(train_df, n_augment=2)

from neuralforecast import NeuralForecast
from neuralforecast.models import NHITS

nf = NeuralForecast(
    models=[NHITS(h=14, input_size=28, max_steps=100)],
    freq="D",
)
nf.fit(df=synthetic_df)
neural_forecasts = nf.predict()

from mlforecast import MLForecast
from sklearn.linear_model import LinearRegression

mlf = MLForecast(
    models=LinearRegression(),
    freq="D",
    lags=[1, 7, 14],
)
mlf.fit(synthetic_df)
ml_forecasts = mlf.predict(h=14)

from statsforecast import StatsForecast
from statsforecast.models import AutoETS

sf = StatsForecast(
    models=[AutoETS(season_length=7)],
    freq="D",
)
sf.fit(synthetic_df)
statistical_forecasts = sf.predict(h=14)