System and method for improvements to pre-processing of data for forecasting
An on-premises system for pre-processing data for forecasting according to an embodiment includes at least one processor and at least one memory having a plurality of instructions stored thereon that, in response to execution by the at least one processor, causes the on-premises system to receive a request to forecast contact center data using a cloud system, determine a first number of interactions per unit of time for a source interval, determine a second number of units of time in a destination interval, and determine a third number of interactions in the destination interval based on the first number of interactions per unit of time for the source interval and the second number of units of time in the destination interval.
1. A system for pre-processing data for forecasting, the system comprising:
a data storage having stored thereon time series data in a source data structure;
at least one processor; and
at least one memory having a plurality of instructions stored thereon that, in response to execution by the at least one processor, causes the system to:
determine a corresponding first number of data points per unit of time for a corresponding source interval of the time series data for each source interval represented in at least a portion of the source data structure;
determine a corresponding second number of units of time in a corresponding destination interval represented in an output data structure of a computing system;
determine a corresponding third number of data points in the corresponding destination interval based on the corresponding first number of data points per unit of time for the corresponding source interval and the corresponding second number of units of time in the corresponding destination interval for each source interval represented in the at least the portion of the source data structure; and
store to the output data structure of the computing system, for each corresponding destination interval and without use of a third data structure intermediate to the source data structure and the output data structure, data representative of the corresponding third number of data points determined for the corresponding destination interval.
2. The system of claim 1 , wherein to determine the corresponding first number of data points per unit of time comprises to determine the corresponding first number of data points per unit of time in response to receipt of a request to forecast time series data using the computing system.
3. The system of claim 2 , wherein the computing system comprises a cloud-based computing system; and
wherein the request to forecast the time series data comprises a request to migrate the time series data from an on-premises system to the cloud-based computing system.
4. The system of claim 2 , wherein the request to forecast time series data comprises an on-demand user request.
5. The system of claim 1 , wherein to determine the corresponding first number of data points per unit of time comprises to determine the corresponding first number of data points per unit of time in response to a periodic trigger for pre-processing data for forecasting according to a predefined interval.
6. The system of claim 1 , wherein to determine the corresponding third number of data points in the destination interval comprises to multiply the corresponding first number of data points per unit of time for the corresponding source interval by the corresponding second number of units of time in the corresponding destination interval.
7. The system of claim 1 , wherein the source data structure includes time series data associated with at least a first source interval having a first interval length and a second source interval having a second interval length different from the first interval length.
8. The system of claim 1 , wherein the plurality of instructions further causes the system to fill gaps in the time series data represented in the at least the portion of the source data structure.
9. The system of claim 1 , further comprising a contact center system; and
wherein the contact center system comprises the at least one processor and the at least one memory.
10. The system of claim 1 , wherein the computing system is remotely located relative to the at least one processor and the at least one memory.
11. A method of pre-processing data for forecasting, the method comprising:
determining, by a computing system, a corresponding first number of data points per unit of time for a corresponding source interval of time series data for each source interval represented in at least a portion of a source data structure;
determining, by the computing system, a corresponding second number of units of time in a corresponding destination interval represented in an output data structure;
determining, by the computing system, a corresponding third number of data points in the corresponding destination interval based on the corresponding first number of data points per unit of time for the corresponding source interval and the corresponding second number of units of time in the corresponding destination interval for each source interval represented in the at least the portion of the source data structure; and
storing to the output data structure, for each corresponding destination interval and without use of a third data structure intermediate to the source data structure and the output data structure, data representative of the corresponding third number of data points determined for the corresponding destination interval.
12. The method of claim 11 , wherein determining the corresponding first number of data points per unit of time comprises determining the corresponding first number of data points per unit of time in response to receiving a request to forecast time series data using the computing system.
13. The method of claim 12 , wherein the computing system comprises a cloud-based computing system; and
wherein receiving the request to forecast the time series data comprises receiving a request to migrate the time series data from an on-premises system to the cloud-based computing system.
14. The method of claim 12 , wherein receiving the request to forecast time series data comprises receiving an on-demand user request.
15. The method of claim 11 , wherein determining the corresponding first number of data points per unit of time comprises determining the corresponding first number of data points per unit of time in response to a periodic trigger for pre-processing data for forecasting according to a predefined interval.
16. The method of claim 11 , wherein determining the corresponding third number of data points in the destination interval comprises multiplying the corresponding first number of data points per unit of time for the corresponding source interval by the corresponding second number of units of time in the corresponding destination interval.
17. The method of claim 11 , wherein the source data structure includes time series data associated with at least a first source interval having a first interval length and a second source interval having a second interval length different from the first interval length.
18. The method of claim 11 , further comprising filling gaps in the time series data represented in the at least the portion of the source data structure.
19. The method of claim 11 , wherein the computing system comprises a contact center system.
20. The method of claim 19 , wherein the corresponding first number of data points comprises one of (i) a number of calls offered to agents of the contact center system, (ii) a number of calls handled by agents of the contact center system, or (iii) a total handle time of calls by agents of the contact center system.