US 11,443,015 B2 · Generating prediction models in accordance with any specific data sets

Abstract

Embodiments of the present invention provide systems, methods, and computer storage media for facilitating generation of prediction models. In some embodiments, a predetermined number of parameter value sets is identified. Each parameter value set includes a plurality of parameter values that represent corresponding parameters within a time series model. The parameter values can be selected in accordance with stratified sampling to increase a likelihood of prediction accuracy. The parameter value sets are input into a time series model to generate a prediction value in accordance with observed time series data, and the parameter value set resulting in a least amount of prediction error can be selected and used to generate a time series prediction model (ARIMA, AR, MA, ARMA) with corresponding model parameters, such as p, q, and/or k, subsequently used to predict values.

One or more non-transitory computer storage media storing computer-useable instructions that, when used by one or more computing devices, cause the one or more computing devices to perform operations to facilitate generation of prediction models, the method comprising:

identifying a predetermined number of parameter value sets, each parameter value set having parameter values that represent corresponding parameters for inputting as a combination to define a function in an autoregressive integrated moving average (ARIMA) prediction model, wherein the parameter values indicate one of a number of differencing terms or an order of differencing terms to use in the corresponding function of the ARIMA predication model, and an indication of a previous number of web page views, each parameter value within each parameter value set is selected by:
selecting a value from one of a plurality of strata, for a corresponding parameter, based on probabilities associated with each strata of the plurality of strata for the corresponding parameter, wherein each strata of the plurality of strata for the corresponding parameter includes a subportion of a plurality of potential parameter values for the corresponding parameter and the strata is associated with a corresponding probability, used to select the value, determined based on a distribution of previously selected parameter values for previous prediction models;
receiving a first portion of the observed time series data set from a data collection center with data describing web traffic;
in a first iteration, implementing each identified parameter value set into the corresponding ARIMA prediction model and use the first portion of the observed time series data set to generate a predicted value associated with each parameter value set;
comparing the predicted value associated with each parameter value set to a second portion of the observed time series data set that corresponds with the predicted value to generate a first relative prediction error for each parameter value set, wherein the first portion does not overlap with the second portion;
in a second iteration, implementing each identified parameter value set into the corresponding ARIMA prediction model and use a third portion of the observed time series data set to generate a new predicted value associated with each parameter value set, wherein the third portion partially overlaps with the second portion;
comparing the new predicted value associated with each parameter value set to a fourth portion of the observed time series data set that corresponds with the predicted value to generate a second relative prediction error for each parameter value set, wherein the fourth portion does not overlap with the third portion;
selecting a parameter value set associated with a least relative prediction error, from the first and the second relative prediction errors for each parameter value set, for generating a time series prediction model;
implementing the selected parameter value set into the ARIMA prediction model to generate the time series prediction model;
and
using the time series prediction model to predict values expected to occur at a later time.

8 more elements

Generating prediction models in accordance with any specific data sets

Life of this patent

Generating prediction models in accordance with any specific data sets

Life of this patent

Abstract

Claims20