Determining the value of an item or event at a point in time or over a targeted period of time based on the values of the item over a past era. During the past era, data is obtained to represent the changes to the value during the era. This data is iteratively processed by: smoothing the curve, subtracting the smooth curve from the original curve to obtain a next level curve and repeating the process. After several iterations, a set of smoothed out curves are obtain and the projected time-wise to encompass the targeted time or period of time. Once projected, the curves are then combined together and the value at the targeted time or period of time can then be ascertained.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method, under the control of a processing unit, for identifying the value of characteristics of an item at a point in time based on characteristic information representative of the item during a past era of time, the method comprising the actions of the processing unit: receiving chart data representative of one or more characteristics of an item during a past era of time; iteratively extracting varying degrees of characteristic information from the chart data for n levels; for each degree of characteristic information, project the data to a different point in time; and iteratively combining the projected data to create new chart data representative of one or more characteristics of the item at a different point in time; and further comprising the action of invoking an action based at least in part on the value of the one or more characteristics at the different point in time. 2. The method of claim 1, wherein the action of iteratively extracting varying degrees of characteristic information from the chart data for n levels comprises the actions of: (a) identifying the trend of the data represented in the current level chart data; (b) extracting the characteristic information from the current level chart data as a function of the identified trend of the data and the current level curve data; (c) setting a next level chart data to be the characteristic information for the current level; (d) using the next level chart data and repeating actions (a)-(c); and (e) after n iterations, set the margin of error as a function of the characteristic information and the chart data for the final level. 3. The method of claim 2, wherein the action of projecting the data to a different point in time for each degree of characteristic information comprises the actions of: identifying a target point in time; and projecting a curve represented by the identified trend of data for each level of the chart data at least to that target point in time by continuing the trend and pattern of the data. 4. The method of claim 3, wherein the action of iteratively combining the projected data to create new chart data representative of one or more characteristics of the item at a different point in time comprises the action of combining each level of the projected data at least for the data corresponding with the different point in time. 5. The method of claim 2, wherein the action of identifying the trend of the data represented in the current level chart data comprises the action of smoothing the curve represented by the chart data. 6. The method of claim 5, wherein the action of smoothing the curve comprises calculating a moving average. 7. The method of claim 2, wherein the action of extracting the characteristic information from the current level chart data as a function of the identified trend of the data and the current level curve data comprises subtracting the identified trend of the data from the curve represented by the chart data. 8. The method of claim 2, wherein the action of generating a next level chart data as a function of the characteristic information for the current level and the chart data for the current level comprises the action of subtracting the characteristic information from the current level from the chart data for the current level. 9. The method of claim 1, wherein the action of projecting the data to a different point in time for each degree of characteristic information comprises the actions of: identifying a target point in time; and projecting a curve represented by the characteristic information for each level at least to that target point in time by continuing the trend and pattern of the data. 10. The method of claim 1, wherein the action of iteratively combining the projected data to create new chart data representative of one or more characteristics of the item at a different point in time comprises the actions of: combining each level of the projected data at least for the data corresponding with the different point in time. 11. The method of claim 1, further comprising the action of attributing any remaining characteristic information for the nth iteration as the margin of error. 12. The method of claim 11, wherein the iterative process is repeated until the margin of error is substantially negligible. 13. The method of claim 1, wherein the action of iteratively extracting varying degrees of characteristic information from the chart data for n levels comprises the actions of: (a) smoothing the curve of the data represented in the current level chart data; (b) setting a next level chart data to be the difference between the smoothed curve and the original chart data; and (c) using the next level chart data and repeating actions (a)-(b). 14. The method of claim 13, wherein the action of smoothing the curve comprises calculating a moving average. 15. The method of claim 13, wherein the action of projecting the smoothed data to include the target point or period of time comprises: determining if there is a repeatable pattern within the smoothed data over time period t and repeating the pattern over additional time periods t until the target point or period is included; and if no repeatable pattern is identified, determining if there is a trend within the smoothed data over time period t and continuing the trend over additional time periods t until the target point or period is included. 16. A method, implemented within a computing environment, configured to approximate characteristics of an item for a target point or period time based on characteristic information representative of the item during a past era of time, the method comprising the actions of: receiving data representative of one or more characteristics of an item during a past era of time; extracting varying degrees of characteristic information from the characteristic data by: applying a smoothing algorithm to the characteristic data over at least a portion of the past era; subtract the smoothed data from the characteristic data to obtain remainder data; repeat the applying action and subtracting action two or more times with the remainder data; for each set of smoothed data, projecting the smoothed data to include the target point or period of time; combining the projected smoothed data to create projected characteristic data representative of the one or more characteristics of the item; and invoking an action based at least in part on the value of the one or more characteristics at the different point in time. 17. The method of claim 16, wherein the action of applying a smoothing algorithm comprises calculating a moving average. 18. The method of claim 16, wherein the action of projecting the smoothed data to include the target point or period of time comprises: identifying a pattern within the smoothed data over time period t; repeating the pattern over additional time periods t until the target point or period is included. 19. The method of claim 16, wherein the action of projecting the smoothed data to include the target point or period of time comprises: identifying a trend within the smoothed data over time period t; continuing the trend over additional time periods t until the target point or period is included. 20. The method of claim 16, wherein the action of projecting the smoothed data to include the target point or period of time comprises: determining if there is a repeatable pattern within the smoothed data over time period t and repeating the pattern over additional time periods t until the target point or period is included; and if no repeatable pattern is identified, determining if there is a trend within the smoothed data over time period t and continuing the trend over additional time periods t until the target point or period is included.