import pytest
import numpy as np
import pandas as pd
from statsmodels.tsa.statespace.structural import UnobservedComponents

def test_unobserved_components_fit():
    # Simulate monthly returns (short history: 24 observations)
    np.random.seed(42)
    dates = pd.date_range('2020-01-01', periods=24, freq='ME')
    returns = np.random.normal(0.01, 0.04, size=24) + np.sin(np.linspace(0, 4*np.pi, 24))*0.02
    series = pd.Series(returns, index=dates)

    # Using 'local linear trend' and 'seasonal' with period=12
    model = UnobservedComponents(series, level='local linear trend', seasonal=12)
    res = model.fit(disp=False)
    
    assert res is not None
    
    # Check forecast
    fc = res.get_forecast(steps=1)
    forecast = fc.predicted_mean.iloc[0]
    se = fc.se_mean.iloc[0]
    
    # Assert values are reasonable and not NaN
    assert not np.isnan(forecast)
    assert not np.isnan(se)
    assert se > 0