from sktime.pipeline import make_pipeline
from sktime.transformations.series.adapt import TabularToSeriesAdaptor
from sktime.forecasting.arima import ARIMA
from sktime.datasets import load_airline
from sklearn.preprocessing import StandardScaler

# Load sample dataset
y = load_airline()

# Define the scaler (wrapped to adapt to time series)
scaler = TabularToSeriesAdaptor(StandardScaler())

# Define the forecaster
forecaster = ARIMA(order=(1, 1, 1))

# Create the pipeline using make_pipeline
pipeline = make_pipeline(scaler, forecaster)

# Fit the pipeline
pipeline.fit(y)

# Forecast for 12 steps ahead
y_pred = pipeline.predict(fh=12)
print(y_pred)