Table 3 Comparison of log-likelihood, AIC, BIC, autocorrelation, moving average, and Sigma coefficients of different models for selecting the best-fitted model in forecasting of hospitalization days
From: Time series analysis for forecasting neonatal intensive care unit census and neonatal mortality
Log likelihood | AR(SE) | MA(SE) | Sigma(SE) | AIC | BIC | |
|---|---|---|---|---|---|---|
LN. smooth length of stay SARIMA (1,0,4,4) | 51.17 | 0.563 (0.173) | 0.487(0.58) | 0.119 (0.530) | -88.342 | -71.33 |
LN. smooth length of stay SARIMA (4,0,1,4) | 48.91 | 0.563 (0.173) | − 0.426 (0.99) | 0.0.113 (0.530) | -92.19 | -76.19 |
LN. smooth length of stay SARIMA (1,0,1,4) | 45.88 | -0.153 (0.454) | 0.462 (0.376) | 0.139 (0.013) | -83.77 | -74.04 |
LN. smooth length of stay SARIMA (4,1,4,4) | 54.09 | − 0.519 (0.225) | 0.179 (0.523) | 0.0958 (0.029) | -83.82 | -73.14 |
LN. smooth length of stay SARIMA (4,1,1,4) | 49.26 | -0.294 (0.163) | -1.000009 3121.368 | 0.118 (0.592) | -84.501 | -67.839 |
LN. smooth length of stay SARIMA (1,1,1,4) | 45.44 | 0.125 (0.127) | -1 | 0.128 (0.011) | -84.88 | -77.74 |
LN. smooth length of stay SARIMA (1,1,4,4) | 51.17 | -0.789 (0.258) | -0.426 (0.1) | 0.119 (0.530) | -86.29 | -71.99 |