This message was deleted.

That's indeed caused by the transformations. If you have a serie with 10 samples and want to use lag15 you'll get a NaN. If you're fine with that you can define a pipeline instead like:

from sklearn.impute import SimpleImputer
from sklearn.pipeline import make_pipeline

rf = make_pipeline(SimpleImputer(), RandomForestRegressor())  # use this as your model instead

Or you can use a before predict callback that fills nans

def fill_na(df):
    return df.fillna(0)  # or some other value

MLforecast.predict(h, before_predict_callback=fill_na)

but all my series have 45 samples

But you're using the rolling mean of the lag 12 over a window of size 12, which needs 25 samples. Since you're using h=6, step_size=12 and n_windows=2 the first training set removes 6 + 12 = 18 samples. So if you have 45 then the first training set has 45 - 18 = 27. If you're using the first difference that drops another one, so you end up with 26. Are you sure all your series have 45? From the errors seems like some end up with less samples

hmm I see, that does make a lot of sense

Can you provide a reproducible example? I just tried this and it runs fine:

import numpy as np
from mlforecast import MLForecast
from mlforecast.target_transforms import GlobalSklearnTransformer, LocalStandardScaler, Differences
from mlforecast.utils import generate_series
from numba import njit
from sklearn.ensemble import RandomForestRegressor
from sklearn.preprocessing import FunctionTransformer
from window_ops.expanding import expanding_mean
from window_ops.rolling import rolling_mean

@njit
def rolling_mean_12(x):
    return rolling_mean(x, window_size=12)

sk_log1p = FunctionTransformer(np.log1p, np.expm1)
series = generate_series(2, min_length=45, max_length=45)

lags = [1,2,3,6,12]
lag_transforms = {
    1: [expanding_mean, rolling_mean_12],
    2: [expanding_mean, rolling_mean_12],
    3: [expanding_mean, rolling_mean_12],
    6: [expanding_mean, rolling_mean_12],
    12: [expanding_mean, rolling_mean_12]
}
# Crear pipeline de pronosticos
ml = MLForecast(
    models=[LinearRegression()], #3 modelos de ML
    freq='D', #Month start
    lags=lags,
    lag_transforms=lag_transforms, #Lag transforms definidas anteriormente
    target_transforms=[GlobalSklearnTransformer(sk_log1p), LocalStandardScaler(), Differences([1])], #El orden de las
    #transformaciones importa (log, estandarizo y primera diferencia)
    num_threads=1,
    date_features=['month']
ml.cross_validation(df=series, h=6, step_size=12, n_windows=2)
)

yes