import pandas as pd
from sktime.forecasting.base import ForecastingHorizon
from sktime.forecasting.naive import NaiveForecaster

# given 4 hours of data with a seasonal periodicity of 3 hours
sp = 3
train_period = pd.date_range("2021-06-01 00:00", "2021-06-1 04:00", freq="1h", closed="left")
train_data = pd.Series(
    index=train_period,
    data=(list(range(1, sp + 1))*len(train_period))[:len(train_period)],
)
print(train_data)

# 2021-06-01 00:00:00    1
# 2021-06-01 01:00:00    2
# 2021-06-01 02:00:00    3
# 2021-06-01 03:00:00    1
# Freq: H, dtype: int64

# let's forecast the next 5 hours with a periodicity of 3 hours
test_period = pd.date_range("2021-06-01 04:00", "2021-06-1 09:00", freq="1h", closed="left")
fh = ForecastingHorizon(test_period, is_relative=False)
model = NaiveForecaster(strategy="mean", sp=sp)
model.fit(train_data)
test_data = model.predict(fh)
print(test_data)

# 2021-06-01 04:00:00    1.0
# 2021-06-01 05:00:00    2.0
# 2021-06-01 06:00:00    3.0
# 2021-06-01 07:00:00    1.0
# 2021-06-01 08:00:00    2.0
# dtype: float64

# the result I expected is

# 2021-06-01 04:00:00    2.0  # (value of 3 hours earlier)
# 2021-06-01 05:00:00    3.0  # (value of 3 hours earlier)
# 2021-06-01 06:00:00    1.0  # (mean value of 3 and 6 hours earlier)
# 2021-06-01 07:00:00    2.0  # (value of 6 hours earlier)
# 2021-06-01 08:00:00    3.0  # (value of 6 hours earlier)
# dtype: float64