Embodiments of the present invention are directed to facilitating concurrent forecasting associating with multiple time series data sets. In accordance with aspects of the present disclosure, a request to perform a predictive analysis in association with multiple time series data sets is received. Thereafter, the request is parsed to identify each of the time series data sets to use in predictive analysis. For each time series data set, an object is initiated to perform the predictive analysis for the corresponding time series data set. Generally, the predictive analysis predicts expected outcomes based on the corresponding time series data set. Each object is concurrently executed to generate expected outcomes associated with the corresponding time series data set, and the expected outcomes associated with each of the corresponding time series data sets are provided for display.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method comprising: receiving a request to perform a predictive analysis in association with multiple time series data sets, the multiple time series data sets determined from raw machine data; parsing the request to identify each of the time series data sets to use in the predictive analysis; for each time series data set, initiating an object to perform the predictive analysis for the corresponding time series data set, the predictive analysis predicting expected outcomes based on the corresponding time series data set; concurrently executing each object to generate one or more expected outcomes associated with the corresponding time series data set; and providing the one or more expected outcomes associated with each of the corresponding time series data sets for display. 2. The computer-implemented method of claim 1 further comprising converting a set of raw data to the set of time series data. 3. The computer-implemented method of claim 1, wherein the request to perform a predictive analysis comprises a predict command that includes an indication of each of the time series data sets. 4. The computer-implemented method of claim 1, wherein the request to perform the predictive analysis comprises an indication of a first time series data set and a first forecasting algorithm to utilize to perform the predictive analysis based on the first time series data set, and an indication of a second time series data set and a second forecasting algorithm to utilize to perform the predictive analysis based on the second time series data set. 5. The computer-implemented method of claim 1, wherein the request to perform the predictive analysis comprises an indication of a first time series data set, an indication of a second time series data set, and an indication of a forecasting algorithm to utilize to perform the predictive analysis in association with the first time series data set and the second time series data set. 6. The computer-implemented method of claim 1, wherein the request to perform the predictive analysis comprises an indication of a first time series data set and first corresponding parameters to utilize to perform the predictive analysis in association with the first time series data set, and an indication of a second time series data set and second corresponding parameters to utilize to perform the predictive analysis in association with the second time series data set. 7. The computer-implemented method of claim 1, wherein parsing the request further identifies a forecasting algorithm to utilize for each of the time series data sets. 8. The computer-implemented method of claim 1, wherein parsing the request identifies a first forecasting algorithm associated with a first time series data set and a second forecasting algorithm associated with a second time series data set. 9. The computer-implemented method of claim 1, wherein initiating the object for each time series data set comprises initiating a first object for executing the predictive analysis for a first time series data set and initiating a second object for executing the predictive analysis for a second time series data set. 10. The computer-implemented method of claim 1, wherein the generated one or more expected outcomes associated with the corresponding time series data sets are aggregated and provided for concurrent display. 11. The computer-implemented method of claim 1 further comprising determining that the request to perform a predictive analysis comprises a request to perform concurrent predictive analysis for the multiple time series data sets. 12. The computer-implemented method of claim 1, wherein each object performs the predictive analysis for the corresponding time series data set based on a designated forecasting algorithm specified in the received request to perform the predictive analysis. 13. The computer-implemented method of claim 1, wherein each object performs the predictive analysis for the corresponding time series data set by: accessing the corresponding time series data set; and applying a forecasting algorithm designated for the corresponding time series data set to generate the one or more expected outcomes. 14. The computer-implemented method of claim 1, wherein each object performs the predictive analysis for the corresponding time series data set by: accessing the corresponding time series data set; determining that the corresponding time series data set has at least one missing data value; generating a predicted missing value for each of the at least one missing data values; and using the time series data set and the predicted missing values for each of the at least one missing data values to determine periodicity associated with the corresponding time series data set. 15. The computer-implemented method of claim 1, wherein each object performs the predictive analysis for the corresponding time series data set by: determining that the corresponding time series data set has at least one missing data value; generating a predicted missing value for each of the at least one missing data values; using the corresponding time series data set and the predicted missing values for each of the at least one missing data values to determine periodicity associated with the corresponding time series data set; and using the periodicity to generate a forecasting model used to generate the one or more expected outcomes associated with the corresponding time series data set. 16. The computer-implemented method of claim 1, wherein the one or more expected outcomes associated with each of the corresponding time series data sets are concurrently presented to a user. 17. The computer-implemented method of claim 1, wherein the one or more expected outcomes associated with each of the corresponding time series data sets are concurrently presented as a graphical visualization in connection with the corresponding time series data sets. 18. The computer-implemented method of claim 1, wherein the one or more expected outcomes associated with each of the corresponding time series data sets are concurrently presented in a tabular format. 19. One or more computer-readable storage media having instructions stored thereon, wherein the instructions, when executed by a computing device, cause the computing device to: receive a request to perform a predictive analysis in association with multiple time series data sets, the multiple time series data sets determined from raw machine data; parse the request to identify each of the time series data sets to use in the predictive analysis; for each time series data set, initiate an object to perform the predictive analysis for the corresponding time series data set, the predictive analysis predicting expected outcomes based on the corresponding time series data set; concurrently execute each object to generate one or more expected outcomes associated with the corresponding time series data set; and provide the one or more expected outcomes associated with each of the corresponding time series data sets for display. 20. A computing device comprising: one or more processors; and a memory coupled with the one or more processors, the memory having instructions stored thereon, wherein the instructions, when executed by the one or more processors, cause the computing device to: receive a request to perform a predictive analysis in association with multiple time series data sets, the multiple time series data sets determined from raw machine data; parse the request to identify each of the time series data sets to use in the predictive analysis; for each time series data set, initiate an object to perform the predictive analysis for the corresponding time series data set, the predictive analysis predicting expected outcomes based on the corresponding time series data set; concurrently execute each object to generate one or more expected outcomes associated with the corresponding time series data set; and provide the one or more expected outcomes associated with each of the corresponding time series data sets for display.