Systems and methods are provided for performing data mining and statistical learning techniques on a big data set. More specifically, systems and methods are provided for linear regression using safe screening techniques. Techniques may include receiving a plurality of time series included in a prediction hierarchy for performing statistical learning to develop an improved prediction hierarchy. It may include pre-processing data associated with each of the plurality of time series, wherein the pre-processing includes tasks performed in parallel using a grid-enabled computing environment. For each time series, the system may determine a classification for the individual time series, a pattern group for the individual time series, and a level of the prediction hierarchy at which the each individual time series comprises an need output amount greater than a threshold amount. The computing system may generate an additional prediction hierarchy using the first prediction hierarchy, the classification, the pattern group, and the level.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system for performing data mining and statistical learning techniques on a data set, the system comprising: a processor; and a non-transitory computer-readable storage medium including instructions stored thereon, which when executed by the processor, cause the system to perform operations including: receiving a plurality of time series included in a prediction hierarchy for performing statistical learning to develop the prediction hierarchy, each individual time series of the plurality of time series comprising one or more need output characteristics and a need output pattern for an object, the one or more need output characteristics including at least one of a need output data, an intermittence, or a time period of the year, the need output pattern indicating one or more time intervals for which need output for the object is greater than a threshold value; pre-processing data associated with each of the plurality of time series, wherein the pre-processing includes performing tasks in parallel using a grid-enabled computing environment, the tasks comprising, for each time series of the plurality of time series: determining a classification for the individual time series based on the one or more need output characteristics; determining a pattern group for the individual time series by comparing the need output pattern to need output patterns for other time series in the plurality of time series; and determining a level of the prediction hierarchy at which the each individual time series comprises a need output amount greater than a threshold amount; generating an additional prediction hierarchy using the prediction hierarchy, the classification, the pattern group, and the level, wherein utilizing the additional prediction hierarchy generates more accurate need output predictions than need output predictions generated utilizing the prediction hierarchy; and transmitting, to one or more nodes in the grid-enabled computing environment, prediction data related to at least one time series of the plurality of time series based on the additional prediction hierarchy, wherein the additional prediction hierarchy provides user control of the data set when the additional prediction hierarchy applied to the data set and characteristics of the data set. 2. The system of claim 1, wherein the need output amount includes a summation of need output volumes of one or more time series of the plurality of time series. 3. The system of claim 1, further comprising instructions, which when executed by the processor, cause the system to perform operations including: determining a number of low-demand periods within the individual time series, wherein each of the number of low-demand periods is a time period during which need output for the item is less than a threshold value; identifying a number of cycles based on the number of low-demand periods; and determining a preliminary classification for the time series based on the identified number of cycles and the one or more need output characteristics. 4. The system of claim 3, wherein determining the number of low-demand periods within the individual time series include determining an approximate time series utilizing a segmentation algorithm and the individual time series, wherein the number of low-demand periods are determined based on the approximate time series. 5. The system of claim 3, wherein the preliminary classification comprises one of a short-history classification, a low-volume classification, a short time-span non-intermittent classification, a short time-span intermittent classification, a long time-span time period classification, a long time-span non-seasonal classification, a long time-span intermittent classification, a long time-span time period intermittent classification, or a long time-span unclassifiable classification. 6. The system of claim 1, further comprising instructions, which when executed by the processor, cause the system to perform operations including: performing a horizontal reclassification of the individual time series using a classification of one or more sibling time series, the one or more sibling time series belonging to a common parent node in the prediction hierarchy as the individual time series when the individual time series is classified as unclassifiable. 7. The system of claim 6, further comprising instructions, which when executed by the processor, cause the system to perform operations including: determining the horizontal reclassification based on a most frequently used classification among the one or more sibling time series; and assigning the horizontal reclassification to a subset of the individual time series. 8. The system of claim 1, further comprising instructions, which when executed by the processor, cause the system to perform operations including: performing a top-down reclassification of the individual time series using a parent time series of the individual time series as indicated in the prediction hierarchy when the determined classification for the individual time series is long time-span intermittent. 9. The system of claim 1, further comprising instructions, which when executed by the processor, cause the system to perform operations including: generating an initial set of time-series clusters using a first number of clusters, a k-means clustering algorithm, and the plurality of time series; determining an optimal number of clusters using a hierarchical clustering technique applied to the initial set of time-series clusters; and determining an optimal set of time-series clusters using the optimal number of clusters, the k-means clustering algorithm, and the plurality of time series. 10. The system of claim 1, further comprising instructions, which when executed by the processor, cause the system to perform operations including: determining a lowest grouping level of the prediction hierarchy; determining a user-defined grouping level of the prediction hierarchy; and for each level of the prediction hierarchy between, and including, the user-defined grouping level and the lowest grouping level: determining a need output volume amount for one or more sibling time series in the prediction hierarchy; and aggregating the one or more sibling time series in the prediction hierarchy based on the need output volume amount. 11. A computer-program product tangibly embodied in a non-transitory machine-readable storage medium, including instructions configured to cause a data processing apparatus to: receive a plurality of time series included in a prediction hierarchy for performing statistical learning to develop the prediction hierarchy, each individual time series of the plurality of time series comprising one or more need output characteristics and a need output pattern for an object, the one or more need output characteristics including at least one of a need output data, an intermittence, or a time period of the year, the need output pattern indicating one or more time intervals for which need output for the object is greater than a threshold value; pre-process data associated with each of the plurality of time series, wherein the pre-processing includes performing tasks in parallel using a grid-enabled computing environment, the tasks comprising, for each time series of the plurality of time series: determining a classification for the individual time series based on the one or more need output characteristics; determining a pattern group for the individual time series by comparing the need output pattern to need output patterns for other time series in the plurality of time series; and determining a level of the prediction hierarchy at which the each individual time series comprises a need output amount greater than a threshold amount; generate an additional prediction hierarchy using the prediction hierarchy, the classification, the pattern group, and the level, wherein utilizing the additional prediction hierarchy generates more accurate need output predictions than need output predictions generated utilizing the prediction hierarchy; and transmit, to one or more nodes in the grid-enabled computing environment, prediction data related to at least one time series of the plurality of time series based on the additional prediction hierarchy, wherein the additional prediction hierarchy provides user control of the data set when the additional prediction hierarchy applied to the data set and characteristics of the data set. 12. The computer-program product of claim 11, wherein the need output amount includes a summation of need output volumes of one or more time series of the plurality of time series. 13. The computer-program product of claim 11, further comprising instructions configured to cause the data processing apparatus to: determine a number of low-demand periods within the individual time series, wherein each of the number of low-demand periods is a time period during which need output for the item is less than a threshold value; identify a number of cycles based on the number of low-demand periods; and determine a preliminary classification for the time series based on the identified number of cycles and the one or more need output characteristics. 14. The computer-program product of claim 13, wherein determining the number of low-demand periods within the individual time series include determining an approximate time series utilizing a segmentation algorithm and the individual time series, wherein the number of low-demand periods are determined based on the approximate time series. 15. The computer-program product of claim 13, wherein the preliminary classification comprises one of a short-history classification, a low-volume classification, a short time-span non-intermittent classification, a short time-span intermittent classification, a long time-span time period classification, a long time-span non-seasonal classification, a long time-span intermittent classification, a long time-span time period intermittent classification, or a long time-span unclassifiable classification. 16. The computer-program product of claim 11, further comprising instructions configured to cause the data processing apparatus to: perform a horizontal reclassification of the individual time series using a classification of one or more sibling time series, the one or more sibling time series belonging to a common parent node in the prediction hierarchy as the individual time series when the individual time series is classified as unclassifiable. 17. The computer-program product of claim 16, further comprising instructions configured to cause the data processing apparatus to: determine the horizontal reclassification based on a most frequently used classification among the one or more sibling time series; and assign the horizontal reclassification to a subset of the individual time series. 18. The computer-program product of claim 11, further comprising instructions configured to cause the data processing apparatus to: perform a top-down reclassification of the individual time series using a parent time series of the individual time series as indicated in the prediction hierarchy when the determined classification for the individual time series is long time-span intermittent. 19. The computer-program product of claim 11, further comprising instructions configured to cause the data processing apparatus to: generate an initial set of time-series clusters using a first number of clusters, a k-means clustering algorithm, and the plurality of time series; determine an optimal number of clusters using a hierarchical clustering technique applied to the initial set of time-series clusters; and determine an optimal set of time-series clusters using the optimal number of clusters, the k-means clustering algorithm, and the plurality of time series. 20. The computer-program product of claim 11, further comprising instructions configured to cause the data processing apparatus to: determine a lowest grouping level of the prediction hierarchy; determine a user-defined grouping level of the prediction hierarchy; and for each level of the prediction hierarchy between, and including, the user-defined grouping level and the lowest grouping level: determine a need output volume amount for one or more sibling time series in the prediction hierarchy; and aggregate the one or more sibling time series in the prediction hierarchy based on the need output volume amount. 21. A method for performing data mining and statistical learning techniques on a data set, the method comprising: a processor; and a non-transitory computer-readable storage medium including instructions stored thereon, which when executed by the processor, cause the system to perform operations including: receiving a plurality of time series included in a prediction hierarchy for performing statistical learning to develop the prediction hierarchy, each individual time series of the plurality of time series comprising one or more need output characteristics and a need output pattern for an object, the one or more need output characteristics including at least one of a need output data, an intermittence, or a time period of the year, the need output pattern indicating one or more time intervals for which need output for the object is greater than a threshold value; pre-processing data associated with each of the plurality of time series, wherein the pre-processing includes performing tasks in parallel using a grid-enabled computing environment, the tasks comprising, for each time series of the plurality of time series: determining a classification for the individual time series based on the one or more need output characteristics; determining a pattern group for the individual time series by comparing the need output pattern to need output patterns for other time series in the plurality of time series; and determining a level of the prediction hierarchy at which the each individual time series comprises a need output amount greater than a threshold amount; generating an additional prediction hierarchy using the prediction hierarchy, the classification, the pattern group, and the level, wherein utilizing the additional prediction hierarchy generates more accurate need output predictions than need output predictions generated utilizing the prediction hierarchy; and transmitting, to one or more nodes in the grid-enabled computing environment, prediction data related to at least one time series of the plurality of time series based on the additional prediction hierarchy, wherein the additional prediction hierarchy provides user control of the data set when the additional prediction hierarchy applied to the data set and characteristics of the data set. 22. The method of claim 21, wherein the need output amount includes a summation of need output volumes of one or more time series of the plurality of time series. 23. The method of claim 21, further comprising: determining a number of low-demand periods within the individual time series, wherein each of the number of low-demand periods is a time period during which need output for the item is less than a threshold value; identifying a number of cycles based on the number of low-demand periods; and determining a preliminary classification for the time series based on the identified number of cycles and the one or more need output characteristics. 24. The method of claim 23, wherein determining the number of low-demand periods within the individual time series include determining an approximate time series utilizing a segmentation algorithm and the individual time series, wherein the number of low-demand periods are determined based on the approximate time series. 25. The method of claim 23, wherein the preliminary classification comprises one of a short-history classification, a low-volume classification, a short time-span non-intermittent classification, a short time-span intermittent classification, a long time-span time period classification, a long time-span non-seasonal classification, a long time-span intermittent classification, a long time-span time period intermittent classification, or a long time-span unclassifiable classification. 26. The method of claim 21, further comprising: performing a horizontal reclassification of the individual time series using a classification of one or more sibling time series, the one or more sibling time series belonging to a common parent node in the prediction hierarchy as the individual time series when the individual time series is classified as unclassifiable. 27. The method of claim 26, further comprising: determining the horizontal reclassification based on a most frequently used classification among the one or more sibling time series; and assigning the horizontal reclassification to a subset of the individual time series. 28. The method of claim 11, further comprising: performing a top-down reclassification of the individual time series using a parent time series of the individual time series as indicated in the prediction hierarchy when the determined classification for the individual time series is long time-span intermittent. 29. The method of claim 21, further comprising: generating an initial set of time-series clusters using a first number of clusters, a k-means clustering algorithm, and the plurality of time series; determining an optimal number of clusters using a hierarchical clustering technique applied to the initial set of time-series clusters; and determining an optimal set of time-series clusters using the optimal number of clusters, the k-means clustering algorithm, and the plurality of time series. 30. The method of claim 21, further comprising: determining a lowest grouping level of the prediction hierarchy; determining a user-defined grouping level of the prediction hierarchy; and for each level of the prediction hierarchy between, and including, the user-defined grouping level and the lowest grouping level: determining a need output volume amount for one or more sibling time series in the prediction hierarchy; and aggregating the one or more sibling time series in the prediction hierarchy based on the need output volume amount.