自动时间序列预测

# uncomment the following line to install the library
# %pip install statsforecast

from statsforecast.utils import AirPassengersDF

Y_df = AirPassengersDF

import pandas as pd

from statsforecast import StatsForecast
from statsforecast.models import AutoARIMA, AutoETS, AutoTheta, AutoCES

season_length = 12 # Define season length as 12 months for monthly data
horizon = 1 # Forecast horizon is set to 1 month

# Define a list of models for forecasting
models = [
    AutoARIMA(season_length=season_length), # ARIMA model with automatic order selection and seasonal component
    AutoETS(season_length=season_length), # ETS model with automatic error, trend, and seasonal component
    AutoTheta(season_length=season_length), # Theta model with automatic seasonality detection
    AutoCES(season_length=season_length), # CES model with automatic seasonality detection
]

# Instantiate StatsForecast class with models, data frequency ('M' for monthly),
# and parallel computation on all CPU cores (n_jobs=-1)
sf = StatsForecast(
    models=models, # models for forecasting
    freq=pd.offsets.MonthEnd(),  # frequency of the timestamps
    n_jobs=1  # number of jobs to run in parallel, -1 means using all processors
)

# Generate forecasts for the specified horizon using the sf object
Y_hat_df = sf.forecast(df=Y_df, h=horizon) # forecast data
# Display the first few rows of the forecast DataFrame
Y_hat_df.head() # preview of forecasted data

sf.fit(df=Y_df) # Fit the models to the data using the fit method of the StatsForecast object

sf.fitted_ # Access fitted models from the StatsForecast object

Y_hat_df = sf.predict(h=horizon) # Predict or forecast 'horizon' steps ahead using the predict method

Y_hat_df.head() # Preview the first few rows of the forecasted data