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Industry Insights

Tips & Tricks: Creating an Index in MetrixND

March 05, 2014

"Comparing multiple economic drivers in MetrixND can be difficult when the data series are developed in different units. Employment, reported in people, compared to GDP, reported in dollars, can wreak havoc on a graph and present useless information. When using the Graph Object as shown below, the employment and GDP comparison is meaningless when each driver is shown in their respective units.

01

For a meaningful comparison, the economic drivers should be converted to unitless indices before using the Graph Object. Create the indices as variables by dividing each driver by the first period value of the driver. In this example, we divide the employment and GDP series by their January 1999 values. The result of the transformed drivers is a 1.0 based index that shows a meaningful comparison in the Graph Object as shown below.

02

The transformation equation uses MetrixND’s Value function to obtain the January 1999 value for each of the series. The value function is designed to obtain the numerical value from a series based on the assigned year and period number.

The Employment transformation is shown below.
 

Table.Employment/Value(Table.Employment,1999,1)

In this transformation, the Value function is defined to access the Table.Employment variable, and obtain the value in 1999, first period. A similar equation is used for the GDP variable as shown below.

Table.GDP/Value(Table.GDP,1999,1)

While creating indices is a useful way to view the data, these indices can also be used for multiple purposes. Indices may be used directly in a model or creating a weighted average index. Try creating and using indices in your models as you develop forecasts.

By Mark Quan

Principal Forecast Consultant

Mark Quan is a Principal Forecast Consultant with Itron’s Forecasting Division. Since joining Itron in 1997, Quan has specialized in both short-term and long-term energy forecasting solutions as well as load research projects. Quan has developed and implemented several automated forecasting systems to predict next day system demand, load profiles, and retail consumption for companies throughout the United States and Canada. Short-term forecasting solutions include systems for the Midwest Independent System Operator (MISO) and the California Independent System Operator (CAISO). Long-term forecasting solutions include developing and supporting the long-term forecasts of sales and customers for clients such as Dairyland Power and Omaha Public Power District. These forecasts include end-use information and demand-side management impacts in an econometric framework. Finally, Quan has been involved in implementing Load Research systems such as at Snohomish PUD. Prior to joining Itron, Quan worked in the gas, electric, and corporate functions at Pacific Gas and Electric Company (PG&E), where he was involved in industry restructuring, electric planning, and natural gas planning. Quan received an M.S. in Operations Research from Stanford University and a B.S. in Applied Mathematics from the University of California at Los Angeles.