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.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. One or more 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 within a time series model, wherein each parameter value within each parameter value set is selected in accordance with stratified sampling to increase a likelihood of prediction accuracy; implementing each identified parameter value set into the time series model to generate a prediction value in accordance with a set of observed time series data; using the prediction values to select a parameter value set, from among the parameter value sets, that results in a least amount of prediction error; and utilizing the selected parameter value set to generate a time series prediction model, wherein the time series prediction model is subsequently used to predict values expected to occur at some future point in time. 2. The one or more computer storage media of claim 1, wherein each parameter value within each parameter value set is selected in accordance with a given range of parameter values for each parameter. 3. The one or more computer storage media of claim 1, wherein the generation of the time series prediction model is initiated based on a request for one or more prediction values. 4. The one or more computer storage media of claim 1, wherein the parameters within the time series model comprise a number of differencing terms, a number of previous values of a metric to be predicted, and a number of lagged white noise terms. 5. The one or more computer storage media of claim 1, wherein the time series model comprises an autoregressive integrated moving average (ARIMA) model. 6. The one or more computer storage media of claim 1, wherein the stratified sampling comprises: for each parameter, dividing a set of potential parameter values into a plurality of strata; assigning a weight to each strata; and selecting a parameter value for each parameter in accordance with the weights associated with each strata for the parameter. 7. The one or more computer storage media of claim 1, wherein determining the parameter value set resulting in the least amount of prediction error comprises comparing the prediction value with a corresponding observed time series data. 8. The one or more computer storage media of claim 1, wherein the time series prediction model comprises an autoregressive integrated moving average (ARIMA) model. 9. The one or more computer storage media of claim 1, wherein the time series prediction model comprises a moving average model or an autoregressive model. 10. A method to facilitate generation of prediction models, the method comprising: identifying, by a first computing process, a predetermined number of parameter value sets, each parameter value set having parameter values that represent corresponding parameters within a time series model, wherein each parameter value within each parameter value set is selected in accordance with stratified sampling to increase a likelihood of prediction accuracy; implementing, by a second computing process, each identified parameter value set into the time series model to generate a prediction value in accordance with a set of observed time series data; determining, by a third computing process, a parameter value set, from among the parameter value sets, resulting in a least amount of prediction error; and utilizing, by a fourth computing process, the parameter value set to generate a time series prediction model, wherein the time series prediction model is subsequently used to predict values expected to occur at some future point in time, wherein the first, second, third, and fourth computing processes are performed by one or more processors. 11. The method of claim 10, wherein each parameter value within each parameter value set is selected in accordance with a given range of parameter values for each parameter. 12. The method of claim 10, wherein the parameters within the time series model comprise a number of differencing terms, a number of previous values of a metric to be predicted, and a number of lagged white noise terms. 13. The method of claim 10, wherein the parameters within the time series model comprise a number of differencing terms, a number of previous values of a metric to be predicted, and a number of lagged white noise terms. 14. The method of claim 10, wherein the time series model comprises an autoregressive integrated moving average (ARIMA) model. 15. The method of claim 10, wherein determining the parameter value set resulting in the least amount of prediction error comprises comparing the prediction value with a corresponding observed time series data. 16. A system comprising: one or more processors; and one or more computer storage media storing computer-useable instructions that, when used by the one or more processors, cause the one or more processors to: identify a predetermined number of parameter value sets, each parameter value set having parameter values that represent corresponding parameters within an autoregressive integrated moving average (ARIMA) model, wherein each parameter value within each parameter value set is selected in accordance with stratified sampling to increase a likelihood of prediction accuracy; implement each identified parameter value set into the autoregressive integrated moving average model and use a first portion of an observed time series data set to generate a predicted value associated with each parameter value set; compare 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 relative prediction error for each parameter value set; select the parameter value set associated with a least relative prediction error for generating a time series prediction model; implement the selected parameter value set into the autoregressive integrated moving average model to generate the time series prediction model; and use the time series prediction model to predict values expected to occur at a later time. 17. The system of claim 16, wherein the time series prediction model uses at least a portion of an observed time series data set to predict values. 18. The system of claim 16, wherein the time series prediction model comprises the autoregressive integrated moving average model or autoregressive moving average model (ARMA). 19. The system of claim 16, wherein the time series prediction model comprises a moving average model. 20. The system of claim 16, wherein the time series prediction model comprises an autoregressive model.