System and method for analyzing data sets using indexing

A system comprising:

• one or more memories having computer-readable instructions stored thereon;
• and
• one or more processors that execute the computer-readable instructions to:
• obtain merchant data for a plurality of merchants;
• adjust the merchant data to obtain adjusted data based upon a ratio of data types in the merchant data;
• perform a first filtering operation on the plurality of merchants based on the adjusted data for identifying a first subset of small business merchants from the plurality of merchants;
• perform a second filtering operation on the first subset of small business merchants for identifying a second subset of small business merchants from the first subset of small business merchants, wherein the second filtering operation is based at least upon a volume of transactions of the small business merchants;
• apply one or more rules to the adjusted data of the second subset of small business merchants associated with a pre-determined criteria to obtain processed data for the second subset of small business merchants;
• calculate an index value for the second subset of small business merchants, wherein the index is calculated as a function of the processed data of the second subset of small business merchants and a historical baseline;
• and
• generate a report analyzing a trend based on the index value, the report comprising additional information for the second subset of small business merchants.

A computer-implemented method of training a neural network for generating economic forecast data structures comprising:

• collecting a first set of index values and economic data;
• creating a first training set for a first stage of training comprising the collected first set of index values and the collected economic data;
• training the neural network in the first stage of training using the first training set;
• executing the neural network using as input a second set of index values to generate an economic data forecast data structure including forecasted economic data;
• creating a second training set for a second stage of training comprising the first training set and a subset of the forecasted economic data selected based on a loss calculated using a difference between the subset of the forecasted economic data and measured economic data;
• and
• training the neural network in the second stage of training using the second training set.