Systems, methods, and non-transitory computer-readable media can receive a time series data set. At least one simulated forecast time period for the time series data set can be determined. One or more parameters for generating one or more forecasting models for the time series data set can be determined. The one or more forecasting models can be generated based at least in part on the one or more parameters and on the at least one simulated forecast time period. The one or more forecasting models can be evaluated to determine an optimal forecasting model for the time series data set.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method comprising: receiving, by a computing system, a time series data set; determining, by the computing system, at least one simulated forecast time period for the time series data set; determining, by the computing system, one or more parameters for generating one or more forecasting models for the time series data set; generating, by the computing system, the one or more forecasting models based at least in part on the one or more parameters and on the at least one simulated forecast time period; and evaluating, by the computing system, the one or more forecasting models to determine an optimal forecasting model for the time series data set. 2. The computer-implemented method of claim 1, the method further comprising: generating a design matrix for the time series data set, wherein the design matrix stores information describing at least a set of respective observations for each unit identified in the time series data set. 3. The computer-implemented method of claim 1, wherein determining, by the computing system, the at least one simulated forecast time period further comprises: allocating a first portion of observations in the time series data set to a first set of observations, wherein observations in the first set are used to forecast simulated values; and allocating a second portion of observations in the time series data set to a second set of observations, wherein observations in the second set are used to measure a respective accuracies of the simulated values. 4. The computer-implemented method of claim 1, wherein determining, by the computing system, the one or more parameters further comprises: determining a space of kernel parameters; and determining a space of regularization parameters. 5. The computer-implemented method of claim 1, wherein the one or more generated forecasting models each correspond to the at least one simulated forecast time period and each combination of the one or more parameters. 6. The computer-implemented method of claim 1, wherein evaluating, by the computing system, the one or more forecasting models further comprises: determining at least one characteristic of the time series data set; determining at least one different time series data set that corresponds to the at least one characteristic; and determining a respective forecasting accuracy of each model in the one or more forecasting models with respect to the time series data set and the at least one different time series data set. 7. The computer-implemented method of claim 1, wherein evaluating, by the computing system, the one or more forecasting models further comprises: determining a first portion of observations in the time series data set; determining a second portion of observations in the time series data set, wherein observations included in the second portion are different from the observations included in the first portion; generating at least one forecasted value based at least in part on the first portion of observations, wherein the forecasted value corresponds to an observation included in the second portion of observations; and determining a forecasting accuracy for the model based at least in part on a comparison of the at least one forecasted value with a known value corresponding to the observation included in the second portion of observations. 8. The computer-implemented method of claim 1, wherein evaluating, by the computing system, the one or more forecasting models further comprises: generating an error model based at least in part on the evaluation of the one or more forecasting models. 9. The computer-implemented method of claim 8, wherein generating the error model further comprises: measuring respective empirical errors for one or more forecasted values made using at least one forecasting model; and training the error model based at least in part on the one or more forecasted values and the respective empirical errors. 10. The computer-implemented method of claim 8, the method further comprising: generating at least one forecasted value using a model in the one or more forecasting models; and determining, using the error model, a confidence interval for the at least one forecasted value. 11. A system comprising: at least one processor; and a memory storing instructions that, when executed by the at least one processor, cause the system to perform: receiving a time series data set; determining at least one simulated forecast time period for the time series data set; determining one or more parameters for generating one or more forecasting models for the time series data set; generating the one or more forecasting models based at least in part on the one or more parameters and on the at least one simulated forecast time period; and evaluating the one or more forecasting models to determine an optimal forecasting model for the time series data set. 12. The system of claim 11, wherein the instructions, when executed by the at least one processor, further cause the system to perform: generating a design matrix for the time series data set, wherein the design matrix stores information describing at least a set of respective observations for each unit identified in the time series data set. 13. The system of claim 11, wherein determining the at least one simulated forecast time period further comprises: allocating a first portion of observations in the time series data set to a first set of observations, wherein observations in the first set are used to forecast simulated values; and allocating a second portion of observations in the time series data set to a second set of observations, wherein observations in the second set are used to measure a respective accuracies of the simulated values. 14. The system of claim 11, wherein determining the one or more parameters further comprises: determining a space of kernel parameters; and determining a space of regularization parameters. 15. The system of claim 11, wherein the one or more generated forecasting models each correspond to the at least one simulated forecast time period and each combination of the one or more parameters. 16. A non-transitory computer-readable storage medium including instructions that, when executed by at least one processor of a computing system, cause the computing system to perform a method comprising: receiving a time series data set; determining at least one simulated forecast time period for the time series data set; determining one or more parameters for generating one or more forecasting models for the time series data set; generating the one or more forecasting models based at least in part on the one or more parameters and on the at least one simulated forecast time period; and evaluating the one or more forecasting models to determine an optimal forecasting model for the time series data set. 17. The non-transitory computer-readable storage medium of claim 16, wherein the instructions, when executed by the at least one processor, further cause the system to perform: generating a design matrix for the time series data set, wherein the design matrix stores information describing at least a set of respective observations for each unit identified in the time series data set. 18. The non-transitory computer-readable storage medium of claim 16, wherein determining the at least one simulated forecast time period further comprises: allocating a first portion of observations in the time series data set to a first set of observations, wherein observations in the first set are used to forecast simulated values; and allocating a second portion of observations in the time series data set to a second set of observations, wherein observations in the second set are used to measure a respective accuracies of the simulated values. 19. The non-transitory computer-readable storage medium of claim 16, wherein determining the one or more parameters further comprises: determining a space of kernel parameters; and determining a space of regularization parameters. 20. The non-transitory computer-readable storage medium of claim 16, wherein the one or more generated forecasting models each correspond to the at least one simulated forecast time period and each combination of the one or more parameters.