System and method for dynamic time-based user interface
System and method for facilitating management of a time-varying resource pool are provided. The system includes a processor and a memory coupled to the processor. The memory stores processor-executable instructions that, when executed, configure the processor to: obtain a time-series data set including data entries associated with one or more consumed resources; identify one or more recurring resource allocations based on recurring data entries of the time-series data set; identify additional resource allocations based on irregularly-timed data entries of the time-series data set; determine a forecasted resource pool value based on a combination of the identified recurring resource allocations and the additional resource allocations; and upon detection of a trigger condition, generate data for display, via a user interface, a scaled resource allocation value based on the forecasted resource pool value.
1 . A system for facilitating management of a time-varying resource pool, the system comprising:
a processor;
a memory coupled to the processor and storing processor-executable instructions that, when executed, cause the processor to:
obtain a time-series data set including data entries associated with one or more consumed resources;
identify one or more recurring resource allocations based on recurring data entries of the time-series data set;
identify, based on output of two or more learning models, additional resource allocations based on irregularly-timed data entries of the time-series data set, wherein the respective output of each of the two or more learning models is generated based on the time-series data set independently;
determine, based on the respective output associated with the learning model having a lowest error, a forecasted resource pool value based on a weighted combination of the identified recurring resource allocations and the identified additional resource allocations; and
upon detection of a trigger condition, generate data to display, via a user interface, a scaled resource allocation value based on the forecasted resource pool value.
2 . The system of claim 1 , wherein detection of the trigger condition is based on input received on a graphical user interface to navigate to visual elements associated with a one time-based reference.
3 . The system of claim 2 , wherein the one time-based reference includes one or more successive dates along a time spectrum.
4 . The system of claim 2 , comprising a user input device, and wherein the memory includes processor-executable instructions that configure the processor to:
receive, via the user input device, an input associated with an additional resource constraint;
determine an updated forecasted resource pool value based on the additional resource constraint; and
display the scaled resource allocation value based on the updated forecasted resource pool value, the scaled resource allocation value corresponding to the one time-based reference.
5 . The system of claim 2 , wherein the scaled resource allocation value is a weighted value based on the forecasted resource pool value and the one time-based reference.
6 . The system of claim 1 , wherein identifying the one or more recurring resource allocations based on the time-series data set is based on rules-based pattern recognition.
7 . The system of claim 6 , wherein the rules-based pattern recognition includes time-based threshold margins for identifying the one or more recurring resource allocations in the time-series data set.
8 . The system of claim 1 , wherein obtaining the time-series data set is triggered based on detected resource allocations altering the time-varying resource pool value greater than a threshold amount.
9 . The system of claim 1 , wherein the memory includes processor-executable instructions that configure the processor to:
determine that the forecasted resource pool value meets an excess threshold value; and
generate data for display via a user interface to display a notification that the forecasted resource pool value has reached a milestone value.
10 . The system of claim 1 , wherein the trigger condition is based on an elapsed time duration satisfying a threshold value.
11 . A computer-implemented method for facilitating management of a time-varying resource pool, the method comprising:
obtaining a time-series data set including data entries associated with one or more consumed resources;
identifying one or more recurring resource allocations based on recurring data entries of the time-series data set;
identifying, based on output of two or more learning models, additional resource allocations based on irregularly-timed data entries of the time-series data set, wherein the respective output of each of the two or more learning models is generated based on the time-series data set independently;
determining, based on the respective output associated with the learning model having a lowest error, a forecasted resource pool value based on a weighted combination of the identified recurring resource allocations and the additional resource allocations; and
upon detection of a trigger condition, generating data to display, via a user interface, a scaled resource allocation value based on the forecasted resource pool value.
12 . The method of claim 11 , wherein detection of the trigger condition is based on input received on a graphical user interface to navigate to visual elements associated with a one time-based reference.
13 . The method of claim 12 , wherein the one time-based reference includes one or more successive dates along a time spectrum.
14 . The method of claim 12 , comprising:
receiving, via a user input device, an input associated with an additional resource constraint;
determining an updated forecasted resource pool value based on the additional resource constraint; and
displaying the scaled resource allocation value based on the updated forecasted resource pool value, the scaled resource allocation value corresponding to the one time-based reference.
15 . The method of claim 12 , wherein the scaled resource allocation value is a weighted value based on the forecasted resource pool value and the one time-based reference.
16 . The method of claim 11 , wherein identifying the one or more recurring resource allocations based on the time-series data set is based on rules-based pattern recognition.
17 . The method of claim 16 , wherein the rules-based pattern recognition includes time-based threshold margins for identifying the one or more recurring resource allocations in the time-series data set.
18 . The method of claim 11 , wherein obtaining the time-series data set is triggered based on detected resource allocations altering the time-varying resource pool value greater than a threshold amount.
19 . The method of claim 11 , comprising:
determine that the forecasted resource pool value meets an excess threshold value; and
generate data for display via a user interface to display a notification that the forecasted resource pool value has reached a milestone value.
20 . A non-transitory computer-readable medium having stored thereon machine interpretable instructions which, when executed by a processor, cause the processor to perform a computer implemented method of dynamically quantifying data sets associated with a time-varying resource pool value, the method comprising:
obtaining a time-series data set including data entries associated with one or more consumed resources;
identifying one or more recurring resource allocations based on recurring data entries of the time-series data set;
identifying, based on output of two or more learning models, additional resource allocations based on irregularly-timed data entries of the time-series data set, wherein the respective output of each of the two or more learning models is generated based on the time-series data set independently;
determining, based on the respective output associated with the learning model having a lowest error, a forecasted resource pool value based on a weighted combination of the identified recurring resource allocations and the additional resource allocations; and
upon detection of a trigger condition, generating data to display, via a user interface, a scaled resource allocation value based on the forecasted resource pool value.