Improving accuracy of predictions using seasonal relationships of time series data
First 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 time series for performing statistical learning to develop improved object prediction intervals, wherein the time series includes one or more demand characteristics and a demand pattern for an object;
pre-processing data associated with the time series, wherein the pre-processing includes tasks performed in parallel using a grid-enabled computing environment;
determining a number of low demand periods within the time series, a low demand period being a time interval for which demand for the object is less than a threshold value;
determining a series type for the time series using the number of low demand periods;
determining an in-season interval of the time series using the number of low demand periods and the series type, the in-season interval indicating a demand period for which demand for the object has historically been greater than the threshold value;
deriving a future in-season interval using the determined in-season interval, the future in-season interval being a predicted time interval during which demand for the object is predicted to be greater than the threshold value; and
transmitting, to one or more nodes in the grid-enabled computing environment, prediction data related to the time series based on the derived future in-season interval, wherein the derived future in-season interval provides user control of the data set when the derived future in-season interval is applied to the data set and characteristics of the data set.
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Abstract
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.
165 Citations
30 Claims
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1. A system for performing data mining and statistical learning techniques on a data set, the system comprising:
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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 time series for performing statistical learning to develop improved object prediction intervals, wherein the time series includes one or more demand characteristics and a demand pattern for an object; pre-processing data associated with the time series, wherein the pre-processing includes tasks performed in parallel using a grid-enabled computing environment; determining a number of low demand periods within the time series, a low demand period being a time interval for which demand for the object is less than a threshold value; determining a series type for the time series using the number of low demand periods; determining an in-season interval of the time series using the number of low demand periods and the series type, the in-season interval indicating a demand period for which demand for the object has historically been greater than the threshold value; deriving a future in-season interval using the determined in-season interval, the future in-season interval being a predicted time interval during which demand for the object is predicted to be greater than the threshold value; and transmitting, to one or more nodes in the grid-enabled computing environment, prediction data related to the time series based on the derived future in-season interval, wherein the derived future in-season interval provides user control of the data set when the derived future in-season interval is applied to the data set and characteristics of the data set. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A computer-program product tangibly embodied in a non-transitory machine-readable storage medium, the computer-program product including instructions for performing data mining and statistical learning techniques on a data set, the instructions configured to be executed to cause a data processing apparatus to perform operations including:
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receiving a time series for performing statistical learning to develop improved object prediction intervals, wherein the time series includes one or more demand characteristics and a demand pattern for an object; pre-processing data associated with the time series, wherein the pre-processing includes tasks performed in parallel using a grid-enabled computing environment, the tasks comprising; determining a number of low demand periods within the time series, a low demand period being a time interval for which demand for the object is less than a threshold value; determining a series type for the time series using the number of low demand periods; determining an in-season interval of the time series using the number of low demand periods and the series type, the in-season interval indicating a demand period for which demand for the object has historically been greater than the threshold value; deriving a future in-season interval using the determined in-season interval, the future in-season interval being a predicted time interval during which demand for the object is predicted to be greater than the threshold value; and transmitting, to one or more nodes in the grid-enabled computing environment, prediction data related to the time series based on the derived future in-season interval, wherein the derived future in-season interval provides user control of the data set when the derived future in-season interval is applied to the data set and characteristics of the data set. - View Dependent Claims (12, 13, 14, 15, 16, 17, 18, 19, 20)
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21. A computer-implemented method for performing data mining and statistical learning techniques on a data set, comprising:
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receiving a time series for performing statistical learning to develop improved object prediction intervals, wherein the time series includes one or more demand characteristics and a demand pattern for an object; pre-processing data associated with the time series, wherein the pre-processing includes tasks performed in parallel using a grid-enabled computing environment, the tasks comprising; determining a number of low demand periods within the time series, a low demand period being a time interval for which demand for the object is less than a threshold value; determining a series type for the time series using the number of low demand periods; determining an in-season interval of the time series using the number of low demand periods and the series type, the in-season interval indicating a demand period for which demand for the object has historically been greater than the threshold value; deriving a future in-season interval using the determined in-season interval, the future in-season interval being a predicted time interval during which demand for the object is predicted to be greater than the threshold value; and transmitting, to one or more nodes in the grid-enabled computing environment, prediction data related to the time series based on the derived future in-season interval, wherein the derived future in-season interval provides user control of the data set when the derived future in-season interval is applied to the data set and characteristics of the data set. - View Dependent Claims (22, 23, 24, 25, 26, 27, 28, 29, 30)
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Specification