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What | nameofinstance.xml |
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Description | Configuration o the ARMA module |
schema location | http://fews.wldelft.nl/schemas/version1.0/errorModelSets.xsd |
Entry in ModuleDescriptors | <moduleDescriptor id="ErrorModel"> |
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Figure 86 Elements of the error module configuration.
errorModelSet
Root element for definition of an error model set.
inputVariable
Definition of input variable to be used in the error correction model. At least two entries are required in the error model, one for observed time series and one for simulated time series For each entry an input variable will need to be identified. The variableId is used to refer to the time series. See Transformation Module for definition of inputVariable configuration. It is not possible to use LocationSets in the TimeSerieSet definition, so it is required to define an ErrorModelSet per location.
autoOrderMethod
Root element for defining an error model using the AR structure.
Figure 87 Elements of the autoOrderMethod configuration.
orderSelection
Boolean to indicate if order of AR components should be established automatically or if the given order should be used.
order_ar
Order of the AR model. If the orderSelection is true, then this value is the maximum order (may not exceed 50). In literature mostly an value of the AR order up to 3 is chosen, higher values are possible, but will have a smaller contribution to the overall result of the error correction.
order_ma
Not used in this method.
parameters
This optional setting can be used to exactly specify the values for all the parameters (multipliers, powers, dividers, etc) used in the error correction model. An example is shown below. Please note that you will need to establish these parameters firs. One way to do this is to run a long historical run with auto-parameters on. The log file will show the parameters determined by the model. These parameters can be used to fix the parameters for the forecast.
subtractMean
Boolean to indicate if mean of residuals should be subtracted prior to establishing error model.
boxcoxTransformation
Boolean to indicate if the residuals should be transformed using Box-Cox transformation prior to establishing error model.
lambda
Lambda parameter to use in Box-Cox transformation (note: value of 0 means the transformation is a natural logarithm). Values ranging from 0 to 0.5 are often used.
ObservedTimeSeriesId
Input time series set to be defined as the observed data to compare simulated model output to.
SimulatedTimeSeriesId
Input time series set to be defined as the simulated model output for both the historic and the forecast period. Multiple series will be combined into single series. Series with higher index will be overlayed by series with lower index.
OutputTimeSeriesId
Updated timeseries data generated by the error model. This serie can contain data for the historic and the forecast period.
fixedOrderMethod
Root element for defining an error model using the ARMA structure.
Figure 88 Elements of the fixedOrderMethod configuration.
correctionModel
Structure of the error model to be used. The model selection includes the selection of initial transformations. Enumeration of options included;
- none
- ARMA+ systematic
- systematic
- ARMA
- ARMA+ log transformation
- ARMA+ systematic+ log transformation
order_ar
Order of the AR part of the model. In literature mostly an value of the AR order up to 3 is chosen, higher values are possible, but will have a smaller contribution to the overall result of the error correction.
order_ma
Order of the MA part of the model. The order you specify determines the length of the period effected by the moving average function. The higher the order, the longer the effected period. The moving average model is not operational yet.
ObservedTimeSeriesId
Input time series set to be defined as the observed data to compare simulated model output to.
SimulatedTimeSeriesId
Input time series set to be defined as the simulated model output for both the historic and the forecast period. Multiple series will be combined into single series. Series with higher index will be overlayed by series with lower index.
OutputTimeSeriesId
Updated timeseries data generated by the error model. This serie can contain data for the historic and the forecast period.
interpolationOptions
- Interpolation options for filling the missing values of the observed time series. This parameter is optional.
interpolationType
You can make a selection of a type of interpolation. Enumeration of available options is;
- linear ; for linear interpolation between available values
- block ; for block interpolation (note: the last available value is then used until a new value available).
- default ; for replacing unreliable values with a default.
gapLength
Maximum allowed gap size that can be filled using interpolation.defaultValue
Default value required for 'defaultvalue' interpolation option.
maxObserved
Maximum value to be used by the error module. Higher values will be converted to NaN and not used as input for error correction. This parameter is optional.
minObserved
Minimum value to be used by the error module. Lower values will be converted to NaN and not used as input for error correction. This parameter is optional.
maxResult
Maximum value to be generated by the error module. This setting can be used to specify an upper limit of the generated output timeseries. This parameter is optional.
minResult
Minimum value to be generated by the error module. This setting can be used to specify a lower limit of the generated output timeseries. This parameter is optional.
ignoreDoubtful
Should the error module ignore doubtful input values. This parameter is optional.
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