<jupyter_start><jupyter_text>Data set<jupyter_code>df = pd.read_csv("../tests/data/retail_sales.csv")
df["y1"] = df["y"].apply(lambda x: x + 10 * x / 100)
df.head()<jupyter_output><empty_output><jupyter_text>Define the model with same configuration, fit and predict<jupyter_code>m = multi_prophet.MultiProphet(columns=["y", "y1"], growth="linear")
m.fit(df)

future_df = m.make_future_dataframe(periods=10)
forecast = m.predict(future_df)
future_df.tail()
forecast["y"][['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail()

forecast["y1"][['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail()<jupyter_output><empty_output><jupyter_text>Plot results<jupyter_code>plots = m.plot(forecast)<jupyter_output><empty_output><jupyter_text>How the predictions look<jupyter_code>forecast["y"].head()
forecast["y1"].head()<jupyter_output><empty_output><jupyter_text>Define the model with different configuration, fit and predict<jupyter_code>config = {
    "y": {"weekly_seasonality": True, "seasonality_mode": "additive"},
    "y1": {"yearly_seasonality": True, "seasonality_mode": "multiplicative"}
}
m = multi_prophet.MultiProphet(columns=["y", "y1"], config=config)
m.fit(df)

future_df = m.make_future_dataframe(periods=10)
forecast = m.predict(future_df)<jupyter_output>INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
INFO:fbprophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.<jupyter_text>Plot results<jupyter_code>plots = m.plot(forecast)<jupyter_output><empty_output>
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