Interactive chart utilizing shifting control to render shifting of time domains of data series

1. A method of identifying economic indicators for use in business forecasting comprising:

measuring a collection of internal econometric data series;

retrieving at an application server from an internal data source via an aggregation server a first econometric data series selected from the collection of internal econometric data series, wherein each of the internal econometric data series comprises i) a time domain having a plurality of time values, and ii) a value domain having econometric data values for each of the time values in the plurality of time values;

computing a collection of forecasted econometric data series;

retrieving at the application server from an external data source via the aggregation server a second econometric data series selected from the collection of forecasted econometric data series wherein each of the forecasted econometric data series comprises i) a time domain having a plurality of historic time values and a plurality of future time values, and ii) a value domain having actual econometric data values for each of the time values in the plurality of historic time values and forecasted econometric data values for each of the time values in the plurality of future time values;

transmitting the first and second economic data series from the application server for display on a graphical display in a chart comprising:

the first econometric data series plotted on the chart in a first position; the second econometric data series plotted on the chart; and

a time domain shifting control for transposing the time domain of the first econometric data series;

receiving a shift magnitude and a shift direction at the time domain shifting control displayed on the graphical display;

generating a first time shift plot by replotting the first econometric data series in the chart in a second position by transposing the time domain of the first econometric data series by the shift magnitude and shift direction;

displaying on the graphical display one or more of a leading, lagging, cyclic, countercyclic, procyclic or acyclic relationship between the first and second econometric data series based on the shift magnitude and shift direction;

generating a second time shift plot by replotting the first econometric data series in the chart in a third position wherein the time domain of the first econometric data series further comprises a plurality of future time values corresponding to the plurality of future time values of the time domain of the second econometric data series, and wherein the value domain of the first econometric data series further comprises forecasted econometric data values for each of the time values in the plurality of future time values, wherein the forecasted econometric data values of the value domain of the first econometric data series are derived from:

the displayed one or more of a leading, lagging, cyclic, countercyclic, procyclic or acyclic relationship between the first and second econometric data series; and

the forecasted econometric data values of the value domain of the second econometric data series; and

altering internal finances of an organization to meet regulatory requirements responsive to the forecasted econometric data values.

2. The method of claim 1 wherein the second econometric data series is one or more of GOP, GOP change, unemployment, unemployment change, stock market index price, commodity price, oil price, gasoline price, energy price, foreign trade, domestic production, AOP employment report, Fed activity index, prime rate, LIBOR, interest on required balances, national employment report, job openings, labor turnover, Fed Finance index, producer price index, or Consumer Price Index.

3. The method of claim 1 wherein the first econometric data series is a company metric being one or more of specific stock price, sales, production, inventory, cost of goods sold, demand, EBITA, average hours worked, hourly rate, marketing expense, or advertising expense.

4. The method of claim 1 wherein the method identifies a cyclic or countercyclic forecasting indicator.

5. The method of claim 4 further comprising a cyclic or countercyclic forecasting indicator with a correlation coefficient of between plus or minus 0.1 and 1.0.

6. The method of claim 1 further comprising determining whether lower and upper time domains are within outer boundary parameters, and when said time domains are outside outer boundary parameters, imposing a shift magnitude limit on the shift magnitude receivable at the time domain shifting control.

7. A method of identifying economic indicators for use in business forecasting comprising:

providing an aggregation server programmed with one or more aggregation software routines executing on the aggregation server and configured to:

measure a collection of internal econometric data series;

store in a metrics database the collection of internal econometric data series wherein each of the internal econometric data series comprises i) a time domain having a plurality of time values, and ii) a value domain having econometric data values for each of the time values in the plurality of time values;

compute a collection of forecasted econometric data series; and

store in the metrics database the collection of forecasted econometric data series wherein each of the forecasted econometric data series comprises i) a time domain having a plurality of historic time values and a plurality of future time values, and ii) a value domain having actual econometric data values for each of the time values in the plurality of historic time values and forecasted econometric data values for each of the time values in the plurality of future time values;

storing the collection of internal econometric data series and the collection of forecasted econometric data series in the metrics database via the aggregation server;

providing an application server programmed with one or more application software routines executing on the application server and configured to access the metrics database in connection with a graphical display on a user device;

retrieving at the application server from the metrics database a first econometric data series selected from the collection of internal econometric data series and a second econometric data series selected from the collection of forecasted econometric data series;

transmitting the first and second economic data series from the application server for display on the graphical display in a chart comprising:

the first econometric data series plotted on the chart in a first position;

the second econometric data series plotted on the chart; and

a time domain shifting control for transposing the time domain of the first econometric data series;

receiving a shift magnitude and a shift direction at the time domain shifting control displayed on the graphical display;

generating a first time shift plot by replotting the first econometric data series in the chart in a second position by transposing the time domain of the first econometric data series by the shift magnitude and shift direction;

displaying on the graphical display one or more of a leading, lagging, cyclic, countercyclic, procyclic or acyclic relationship between the first and second econometric data series based on the shift magnitude and shift direction; and

generating a second time shift plot by replotting the first econometric data series in the chart in a third position wherein the time domain of the first econometric data series further comprises a plurality of future time values corresponding to the plurality of future time values of the time domain of the second econometric data series, and wherein the value domain of the first econometric data series further comprises forecasted econometric data values for each of the time values in the plurality of future time values, wherein the forecasted econometric data values of the value domain of the first econometric data series are derived from:

the displayed one or more of a leading, lagging, cyclic, countercyclic, procyclic or acyclic relationship between the first and second econometric data series; and

the forecasted econometric data values of the value domain of the second econometric data series; and

altering internal finances of an organization to meet regulatory requirements responsive to the forecasted econometric data values.

8. The method of claim 7 wherein the second econometric data series is one or more of GOP, GOP change, unemployment, unemployment change, stock market index price, commodity price, oil price, gasoline price, energy price, foreign trade, domestic production, AOP employment report, Fed activity index, prime rate, LIBOR, interest on required balances, national employment report, job openings, labor turnover, Fed Finance index, producer price index, or Consumer Price Index.

9. The method of claim 7 wherein the first econometric data series is a company metric being one or more of specific stock price, sales, production, inventory, cost of goods sold, demand, EBITA, average hours worked, hourly rate, marketing expense, or advertising expense.

10. The method of claim 7 wherein the method identifies a cyclic or countercyclic forecasting indicator.

11. The method of claim 10 further comprising a cyclic or countercyclic forecasting indicator with a correlation coefficient of between plus or minus 0.1 and 1.0.

12. The method of claim 7 further comprising determining whether lower and upper time domains are within outer boundary parameters, and when said time domains are outside outer boundary parameters, imposing a shift magnitude limit on the shift magnitude receivable at the time domain shifting control.