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• SDMXDataStructureSpecificTimeSeries.xsd

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xsd.2_1.SDMXDataStructureSpecificTimeSeries.xsd Maven / Gradle / Ivy

   
   

	
		SDMX Core Time Series Structure Specific Data Module
		The core time series structure specific data module contains the descriptions of a derivation of the base structure specific data set format which only allows for data organised as time series. A data set structure specific according to the data set defined here is a valid base structure specific data set, and can be processed using the same rules as those for the base format. The difference in this structure is that the data is assured to be organised as time series. This allows for variations of the structure specific data message which restrict the data to only be formatted as time series. The entire structure declared for the is data set is abstract, meaning that instances will have to be based on types derived from these structures in schemas created based on the details data structure definition.
	
	
   
   	
   		TimeSeriesDataSetType is the abstract type which defines the base structure for any data structure definition specific time series based data set. A derived data set type will be created that is specific to a data structure definition. Unlike the base format, only one variation of this is allowed for a data structure definition. This variation is the time dimension as the observation dimension. Data is organised into a collection of time series. Because this derivation is achieved using restriction, data sets conforming to this type will inherently conform to the base data set structure as well. In fact, data structure specific here will be identical to data in the base data set when the time dimension is the observation dimension, even for the derived data set types. This means that the data contained in this structure can be processed in exactly the same manner as the base structure. The same rules for derivation as the base data set type apply to this specialized data set.
   	
      
         
				
					
					
					
					
						
					
				
            
				
         
      
   
	
	
		
			TimeSeriesType defines an abstract structure which is used to group a collection of observations which have a key in common, organised by time. The key for a series is every dimension defined in the data structure definition, save the time dimension. In addition to observations, values can be provided for attributes which are associated with the dimensions which make up this series key (so long as the attributes do not specify a group attachment or also have an relationship with the time dimension). It is possible for the series to contain only observations or only attribute values, or both. The same rules for derivation as the base series type apply to this specialized series.
		
		
			
				
					
					
				
				
				
			
		
	
	
	
		
			TimeSeriesObsType defines the abstract structure of a time series observation. The observation must be provided a value for the time dimension. This time value should disambiguate the observation within the series in which it is defined (i.e. there should not be another observation with the same time value). The observation can contain an observed value and/or attribute values. The same rules for derivation as the base observation type apply to this specialized observation.
		
		
			
				
					
				
				
				
					
						The TIME_PERIOD attribute is an explicit attribute for the time dimension. This is declared in the base schema since it has a fixed identifier and representation. Since this data is structured to be time series only, this attribute is always required. If the time dimension specifies a more specific representation of time the derived type will restrict the type definition to the appropriate type.