ARMA, or Auto-Regressive Moving-Average, is a method for error prediction. It predicts future errors between model results and reality using recorded errors between past model outputs and observations.
A model in which future values are forecast purely on the basis of past values of the time series is called an autoregressive (AR) process. A model in which future values are forecast purely on the basis of past shocks (or noise or random disturbances) is called a moving average (MA) process. A model that uses both past values of the time series and past shocks is called an autoregressive-moving average (ARMA) process.
- Calculate the difference between actual observed flows from a subcatchment and the runoff predicted by a model.
- Post-processing forecast predictions.
Both these operations use an ARMA model that defines the type of error calculation (linear or logarithmic) to be performed and the autoregressive (AR) and the moving average (MA) coefficients to be used in the ARMA predictions.
This table describes all the ARMA specific data that defines an ARMA error prediction model.
Fields that are common to the majority of objects can be found in the Common Fields topic.
ARMA Data
Database Table Name: hw_arma
|
Field Name |
Description |
Database Field |
Size |
Precision |
Default |
Error Lower Limit |
Error Upper Limit |
Warning Lower Limit |
Warning Upper Limit |
||
|---|---|---|---|---|---|---|---|---|---|---|---|
| ID | A unique name for the ARMA error prediction model. | arma_type | Text | 64 | 0 | ||||||
| Error calculation |
The type of error calculation can be set to either:
|
error_calc | Text | 20 | Linear | 0 | 0 | 0 | 0 | ||
|
Parameters |
A series of records defining the Autoregressive and Moving average parameters for the model. These are entered on the
ARMA Parameter Editor which is displayed by clicking the
|
params |
Structure |
Double |
|
5 |
|
|
|
|
|
button.