error when using SKforecast: UserWarning: `y` has DatetimeIndex index but no frequency. Index is overwritten with a RangeIndex of step 1

Question

I am trying to use skforecast for time series analysis however I am getting warning telling me that the df has no frequency because the index is not DateTimeIndex but in fact it is.

Here is the code:

import yfinance as yf
import datetime as dt

spxl = yf.Ticker("SPXL")
hist = spxl.history(start="2015-01-01")
hist = hist.asfreq("D")
data = hist.dropna()
type(data.index)
#Output: pandas.core.indexes.datetimes.DatetimeIndex

#Split data into train-val-test
#==============================================================================
data = data.loc['2015-01-01': '2022-12-31']
end_train = '2019-12-31'
end_validation = '2020-12-31'
data_train = data.loc[: end_train, :].copy()
data_val   = data.loc[end_train:end_validation, :].copy()
data_test  = data.loc[end_validation:, :].copy()

#Create forecaster
#==============================================================================
forecaster = ForecasterAutoreg(
                regressor = LGBMRegressor(),
                lags = 7
            )

#Grid search of hyper-parameters and lags
#==============================================================================
#Regressor hyper-parameters
param_grid = {
    'n_estimators': [100, 500],
    'max_depth': [3, 5, 10],
    'learning_rate': [0.01, 0.1]
}

#Lags used as predictors
lags_grid = [7]

Here where the warning is triggered, when creating forecaster:

results_grid_q10 = grid_search_forecaster(
                            forecaster         = forecaster,
                            y                  = data.loc[:end_validation, 'Close'],
                            param_grid         = param_grid,
                            lags_grid          = lags_grid,
                            steps              = 7,
                            refit              = True,
                            metric             = 'mean_squared_error',
                            initial_train_size = int(len(data_train)),
                            fixed_train_size   = False,
                            return_best        = True,
                            verbose            = False
                    )

I can not seem to understand what I am doing wrong!

Answer 1

Just in case someone is facing the same problem with skforecast or any other time series forecasting, here is the solutions by the library creators:

https://github.com/JoaquinAmatRodrigo/skforecast/issues/329