2. Arima
Debido a que proporciona un buen ajuste para distintos tipos de problemas, el modelo de Arima es sin duda uno de los modelos de series temporales más utilizados en la práctica.
Ejemplo:
Ajuste del modelo Arima (p,d,q) para el conjunto de datos AMBV3.
| AMBV3 |
|---|
| 84.06 |
| 83.85 |
| 83.56 |
| 83.47 |
| 83.27 |
| 82.81 |
| 82.20 |
| 82.06 |
| 81.62 |
| 80.77 |
| 81.30 |
| 81.92 |
| 82.75 |
| 82.77 |
| 82.84 |
| 82.82 |
| 82.72 |
| 82.29 |
| 81.18 |
| 80.11 |
| 80.27 |
| 80.21 |
| 79.92 |
| 79.96 |
| 80.19 |
| 80.17 |
| 80.17 |
| 79.85 |
| 81.00 |
| 80.44 |
| 79.96 |
| 79.85 |
| 79.82 |
| 80.11 |
| 80.20 |
| 80.31 |
| 81.18 |
| 80.81 |
| 81.15 |
| 81.32 |
| 81.21 |
| 81.40 |
| 81.10 |
| 81.40 |
| 82.27 |
| 82.15 |
| 81.78 |
| 81.69 |
| 81.34 |
| 81.88 |
Subiremos los datos al sistema.

Realizaremos el análisis ajustando conforme la figura de abajo.

En seguida, haga un clic en calcular para obtener los resultados. También se puede descargar los resultados en un archivo Word.
Los resultados son:
Coeficientes
| Valores | |
|---|---|
| ar1 | 1.2991770 |
| ar2 | -0.3315352 |
| ma1 | -1.1432995 |
| ma2 | 0.1433067 |
Medidas de Exactitud
| Valores | |
|---|---|
| MAPE | 0.4195899 |
| MAD | 0.3415169 |
| MSD | 0.1980571 |
Resultados
| Datos | Ajuste | Residuos |
|---|---|---|
| 84.06 | 83.97594 | 0.08405996 |
| 83.85 | 84.05419 | -0.20419184 |
| 83.56 | 83.80930 | -0.24929614 |
| 83.47 | 83.50654 | -0.03654120 |
| 83.27 | 83.45321 | -0.18320532 |
| 82.81 | 83.23766 | -0.42766339 |
| 82.20 | 82.73172 | -0.53171661 |
| 82.06 | 82.10190 | -0.04189500 |
| 81.62 | 82.04797 | -0.42797245 |
| 80.77 | 81.56606 | -0.79605654 |
| 81.30 | 80.66053 | 0.63947339 |
| 81.92 | 81.43665 | 0.48334558 |
| 82.75 | 82.09832 | 0.65168277 |
| 82.77 | 82.94994 | -0.17994385 |
| 82.84 | 82.81778 | 0.02222283 |
| 82.82 | 82.87305 | -0.05304950 |
| 82.72 | 82.83318 | -0.11317707 |
| 82.29 | 82.71394 | -0.42393938 |
| 81.18 | 82.22084 | -1.04083993 |
| 80.11 | 80.99494 | -0.88494483 |
| 80.27 | 79.94805 | 0.32195173 |
| 80.21 | 80.34001 | -0.13001000 |
| 79.92 | 80.26963 | -0.34962719 |
| 79.96 | 79.94210 | 0.01789635 |
| 80.19 | 80.03927 | 0.15073363 |
| 80.17 | 80.30572 | -0.13571517 |
| 80.17 | 80.24290 | -0.07290330 |
| 79.85 | 80.23729 | -0.38728594 |
| 81.00 | 79.87258 | 1.12742243 |
| 80.44 | 81.25721 | -0.81721024 |
| 79.96 | 80.41750 | -0.45750058 |
| 79.85 | 79.92551 | -0.07551215 |
| 79.82 | 79.88654 | -0.06654408 |
| 80.11 | 79.88400 | 0.22600473 |
| 80.20 | 80.22997 | -0.02997067 |
| 80.31 | 80.28719 | 0.02280863 |
| 81.18 | 80.39805 | 0.78194752 |
| 80.81 | 81.38372 | -0.57371613 |
| 81.15 | 80.80714 | 0.34286168 |
| 81.32 | 81.24309 | 0.07691003 |
| 81.21 | 81.38832 | -0.17831860 |
| 81.40 | 81.22568 | 0.17431784 |
| 81.10 | 81.45735 | -0.35734615 |
| 81.40 | 81.08071 | 0.31929431 |
| 82.27 | 81.47966 | 0.79033756 |
| 82.15 | 82.44500 | -0.29499787 |
| 81.78 | 82.15185 | -0.37185024 |
| 81.69 | 81.72017 | -0.03016952 |
| 81.34 | 81.67519 | -0.33518824 |
| 81.88 | 81.29572 | 0.58427699 |
Previsión
| Previsión | I.C. Mín. 80% | I.C. SUp.80% | I.C. Mín. 95% | I.C. Sup. 95% | |
|---|---|---|---|---|---|
| 51 | 81.98464 | 81.4055 | 82.56379 | 81.09892 | 82.87037 |
| 52 | 82.02483 | 81.1348 | 82.91485 | 80.66365 | 83.38600 |
| 53 | 82.04233 | 80.9153 | 83.16937 | 80.31868 | 83.76599 |
| 54 | 82.05176 | 80.7357 | 83.36782 | 80.03902 | 84.06450 |
| 55 | 82.05820 | 80.5864 | 83.53000 | 79.80728 | 84.30913 |
