System and method for predicting service metrics using historical data

A method for allocating resources for a plurality of time intervals, the method comprising:

• receiving, by an input layer of a machine learning model, a forecasted workload and at least one required service metric value for each of the plurality of time intervals, wherein the forecasted workload is included in a multivariate time series matrix, wherein the time series matrix has a number of cells corresponding to a product of:
• a number of the plurality of time intervals, and one or more features;
• for each interval:
• applying a search algorithm to identify an initial allocation assignment, wherein the initial allocation assignment comprises a count vector, and wherein the initial allocation assignment represents how many resources are suggested for each of a plurality of resource skills;
• inputting the initial allocation assignment to a machine learning algorithm, wherein the machine learning algorithm has been previously trained on historic data of a plurality of past intervals, and wherein the machine learning algorithm comprises propagating the received workload through a sequence of dense layers in the machine learning model, wherein each of the dense layers is followed by a sigmoid activation;
• predicting, for each at least one required service metric, by the machine learning algorithm, an expected service metric value provided by the initial allocation assignment based on at least one element from the multivariate time series matrix;
• adjusting, by the search algorithm, the initial allocation assignment based on a difference between the expected service metric value and the corresponding at least one required service metric value, the adjusting using one or more scalar correction ratios for each of the resource skills;
• iteratively repeating the applying, inputting, predicting, and adjusting operations until one of:
• the expected service metric value predicted for an adjusted allocation assignment is within a predetermined distance of the corresponding at least one required service metric value for the interval;
• or
• a predetermined time has elapsed;
• and
• automatically producing a schedule based on the adjusted allocation assignment.