The SARIMAX model summary provides a comprehensive overview of the time series analysis performed on the variable ‘Total’, with 1268 observations from November 1, 2010, to April 21, 2014. The model used is an ARIMA(1, 1, 1), a type of autoregressive integrated moving average model, which is often used for forecasting time series data.
The results show the following key statistics and diagnostics:
Log Likelihood: The model has a log likelihood of -8626.941, which measures the likelihood of the data given the model.
Akaike Information Criterion (AIC): With a value of 17259.882, the AIC suggests the relative quality of the model, with lower values indicating a better model.
Bayesian Information Criterion (BIC): The BIC value is 17275.315, another criterion for model selection, similar to AIC but with a higher penalty for models with more parameters.
Hannan-Quinn Information Criterion (HQIC): This is another measure for model selection, with a value of 17265.680 in this case.
The coefficients for the autoregressive term (ar.L1) and the moving average term (ma.L1) are 0.3624 and -0.9463, respectively, with the latter showing a strong negative effect. Both coefficients are highly significant, as indicated by their P-values of 0.000.
The variance of the model’s residuals (sigma2) is approximately 48,010, indicating the average squared deviation from the predicted value.
Other diagnostics include:
Ljung-Box Test: With a Q statistic of 4.61 and a P-value of 0.03, this test checks for lack of fit in the model, suggesting that the model fits the data well.
Jarque-Bera Test: The JB statistic is 651.24 with a P-value of 0.00, indicating that the residuals may not follow a normal distribution, given the significant skewness and kurtosis.
Heteroskedasticity: The H statistic is 0.87 with a P-value of 0.16, suggesting that there is no significant heteroskedasticity in the model residuals.
The warning about the covariance matrix being calculated using the outer product of gradients suggests that the standard errors and confidence intervals might not be fully reliable.
Additionally, a future warning indicates that an argument used in the `pd.date_range` function is deprecated, suggesting that the code may need updating to maintain compatibility with future versions of pandas.
These notes provide a concise understanding of the SARIMAX model’s results and its implications for the accuracy and reliability of the time series forecasting.