Patent Application
20150170168
Numerous consumers use the Internet, among things to purchase products on-line, locate special events, read news stories, pay bills or perform on-line banking. Numerous business establishments are connected to the Internet to provide products and services to the consumer or perform business-to-business electronic commerce. E-commerce and Internet applications operate and transmit data over a world-wide interconnected communications network.
Retail sales estimates are provided by Federal agencies such as Census Bureau. Currently, there are known indexes that attempt to monitor the economic conditions of a region, such as the United States. For example, the Consumer Sentiment Index compiled by the University of Michigan and the Consumer Confidence Index compiled by the Conference Board attempt to indicate where the market may be headed. Both indexes are based on surveys where consumers state if they believe the economy will improve or deteriorate during the next few months. While providing a subjective measure of the economic forces, they are subjected to emotions, such as panic and/or exited exuberance. There is a need for an improved method to estimate the economic conditions of a region for a given period of time.
In light of the foregoing background, the following presents a simplified summary of the present disclosure in order to provide a basic understanding of some aspects of the disclosure. This summary is not an extensive overview of the disclosure. It is not intended to identify key or critical elements of the disclosure or to delineate the scope of the disclosure. The following summary merely presents some concepts of the disclosure in a simplified form as a prelude to the more detailed description provided below.
Aspects of the present disclosure are directed to methods and systems of macro-economic indication. In one aspect, a computer implemented method includes electronically, at a computer processor, selectively mapping a plurality of merchant classification data to sub-sector economic data for payment card transactions classification; receiving, at a computer processor, retail transaction data of payment cards and demographic data for a plurality of consumers; storing the retail transaction data and demographic data for the plurality of consumers in a database, wherein each transaction data entry corresponds to a purchase by one of the consumers; electronically, at a computer processor, receiving a plurality of retail transaction data based on the payment card transactions classification over a predetermined period of time to define an economic time series dataset; electronically, at a computer processor, adjusting the economic time series dataset based on an autoregressive integrated moving average; and electronically, at a computer processor, transforming the economic time series dataset after the adjusting step by using regression to define a predefined time period percentage change in the retail transaction data.
In one aspect of the present disclosure, a computer system includes at least one database configured to maintain retail a plurality of transaction data of payment cards and demographic data for a plurality of consumers; and at least one computing device, operatively connected to the at least one database, configured to: selectively map a plurality of merchant classification data to sub-sector economic data for payment card transactions classification; selectively mapping a plurality of merchant classification data to sub-sector economic data for payment card transactions classification; receive retail transaction data of payment cards and demographic data for a plurality of consumers; storing the retail transaction data and demographic data for the plurality of consumers in a database, wherein each transaction data entry corresponds to a purchase by one of the consumers; receive the plurality of retail transaction data based on the payment card transactions classification over a predetermined period of time to define an economic time series dataset; adjust the economic time series dataset based on an autoregressive integrated moving average; and transform the economic time series dataset after the adjusting step by using regression to define a predefined time period percentage change in the retail transaction data.
This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. The Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.
The present disclosure is pointed out with particularity in the appended claims. Features of the disclosure will become more apparent upon a review of this disclosure in its entirety, including the drawing figures provided herewith.
Some features herein are illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings, in which like reference numerals refer to similar elements, and wherein:
Tables 1-3 are an example mapping scheme in which various aspects of the present disclosure may be implemented in accordance with one or more example embodiments.
In the following description of various illustrative embodiments, reference is made to the accompanying drawings, which form a part hereof, and in which is shown, by way of illustration, various embodiments in which aspects of the disclosure may be practiced. It is to be understood that other embodiments may be utilized, and structural and functional modifications may be made, without departing from the scope of the present disclosure.
It is noted that various connections between elements are discussed in the following description. It is noted that these connections are general and, unless specified otherwise, may be direct or indirect, wired or wireless, and that the specification is not intended to be limiting in this respect.
Computing system environment 100 may include computing device 101 having processor 103 for controlling overall operation of computing device 101 and its associated components, including random-access memory (RAM) 105, read-only memory (ROM) 107, communications module 109, and memory 115. Computing device 101 may include a variety of computer readable media. Computer readable media may be any available media that may be accessed by computing device 101, may be non-transitory, and may include volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, object code, data structures, program modules, or other data. Examples of computer readable media may include random access memory (RAM), read only memory (ROM), electronically erasable programmable read only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and that can be accessed by computing device 101.
Although not required, various aspects described herein may be embodied as a method, a data processing system, or as a computer-readable medium storing computer-executable instructions. For example, a computer-readable medium storing instructions to cause a processor to perform steps of a method in accordance with aspects of the disclosed embodiments is contemplated. For example, aspects of the method steps disclosed herein may be executed on a processor on computing device 101. Such a processor may execute computer-executable instructions stored on a computer-readable medium.
Software may be stored within memory 115 and/or storage to provide instructions to processor 103 for enabling computing device 101 to perform various functions. For example, memory 115 may store software used by computing device 101, such as operating system 117, application programs 119, and associated database 121. Also, some or all of the computer executable instructions for computing device 101 may be embodied in hardware or firmware. Although not shown, RAM 105 may include one or more applications representing the application data stored in RAM 105 while computing device 101 is on and corresponding software applications (e.g., software tasks), are running on computing device 101.
Communications module 109 may include a microphone, keypad, touch screen, and/or stylus through which a user of computing device 101 may provide input, and may also include one or more of a speaker for providing audio output and a video display device for providing textual, audiovisual and/or graphical output. Computing system environment 100 may also include optical scanners (not shown). Exemplary usages include scanning and converting paper documents, e.g., correspondence, receipts, and the like, to digital files.
Computing device 101 may operate in a networked environment supporting connections to one or more remote computing devices, such as computing devices 141, 151, and 161. Computing devices 141, 151, and 161 may be personal computing devices or servers that include any or all of the elements described above relative to computing device 101. Computing device 161 may be a mobile device (e.g., smart phone) communicating over wireless carrier channel 171.
The network connections depicted in
The disclosure is operational with numerous other general purpose or special purpose computing system environments or configurations. Examples of well-known computing systems, environments, and/or configurations that may be suitable for use with the disclosed embodiments include, but are not limited to, personal computers (PCs), server computers, hand-held or laptop devices, smart phones, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like.
Computer network 203 may be any suitable computer network including the Internet, an intranet, a wide-area network (WAN), a local-area network (LAN), a wireless network, a digital subscriber line (DSL) network, a frame relay network, an asynchronous transfer mode (ATM) network, a virtual private network (VPN), or any combination of any of the same. Communications links 202 and 205 may be any communications links suitable for communicating between workstations 201 and server 204 (e.g. network control center), such as network links, dial-up links, wireless links, hard-wired links, as well as network types developed in the future, and the like. A virtual machine may be a software implementation of a computer that executes computer programs as if it were a standalone physical machine.
Referring to
The macro-economic indicator system 300 of the present disclosure provides for a numerical estimate of the economic demand during a particular period of time for a particular geographic region. In one implementation, geographic region data may pertain to the United States economic region. The system 300 may include a computer implemented method for macroeconomic indication, including electronically selectively mapping a plurality of merchant classification data to sub-sector economic data for payment card transactions classification; electronically receiving a plurality of retail sales data based on the payment card transactions classification over a predetermined period of time to define an economic time series dataset; electronically adjusting the economic time series dataset based on an autoregressive integrated moving average; and electronically transforming the economic time series dataset after the adjusting step by using linear regression to define a predefined time period percentage change in the periodic data. System 300 includes computer program modules, executed by one or more computers as described with respect to
With continued reference to
In one aspect, the mapping module 301 of system 300 can select the desired set of MCCs in the constellation of MCCs to logically map to the defined NAICS of U.S. Census Bureau economic sub-sectors. In one implementation, mapping module 301 selectively may use 109 MCCs for logic mapping to defined economic sub-sectors such as, motor vehicle and parts dealers; furniture and home furnishings stores; electronics and appliance stores; building material and garden equipment and supplies dealers; food and beverage stores; health and personal care stores; gasoline stations; clothing and clothing accessory stores; sporting goods, hobby, book, and music stores; general merchandise stores; and miscellaneous store retailers. Those skilled in the art will realize that the list of sub-sector is not exhaustive but rather an exemplary listing. Examples of an implementation of MCC mapping to a three-digit NAICS code are provided in Tables 1-3. Nevertheless, other types of mapping are possible.
With reference to
In one implementation, there is a mix of different types of customers in the transactional retail data in database 121. For instances, various customer purchase patterns can add volatility to the retail sales data. To better filter the data for modeling in system 300, sampling module 303 may filter consumer data points by filtering the number of credit card transactions or debit card transaction in a month. In one implementation of sampling module 303, a lower bound can be set a threshold number of transactions per month for each household. For example, one implementation of the process may use five (5) transactions per month. Nevertheless, other number transactions per month can be used for the retail sales estimation. In one implementation, the sampling component 303 has computer logic to take into account that retail payment cards might be shared within a household; as such, sample filtering can be performed over household data rather than individual customer data.
With reference to
With reference to
The module 307 provides for balance because too old data would be non-representative of the recent trends, while too recent data may be too short to see the trends. To account for the seasonality in data, autoregressive integrated moving average (ARIMA) techniques may be implemented in the module 307. For example, the Census Bureau's X12-ARIMA methodology could be utilized which uses a combination of linear regression outputs for the retail sales estimates. The seasonal adjustment method may input parameters for X12 pertaining to uniform input parameters for the identified sub-sectors. In one implementation of module 307, the seasonal adjustments were carried before the linear regression of the data. Nevertheless, the seasonal adjustments may be carried out after the linear regression of the data, if desired.
With reference to
With reference to
Average Absolute Error=Average(Absolute(Census Bureau ActualâMODEL Estimate))over Range(Base Month to Current Month)
A second metric is pertains to the direction match rate. This direction match rate pertains to the model estimate in the positive or negative direction of Census Bureau actual direction; The direction match rate formula is shown below:
Direction Match Rate=# of Direction Match with Actual/# of months
A third metric pertains to the hit rate. The hit rate measures whether the model estimate is on the correct side of consensus or not. The hit rate formula is shown below:
Hit Rate=# of times Actual and BAC Estimate were on the same side of Consensus Estimate from Bloomberg/# of months
Referring to
Referring to
In yet other implementations of the system 300, cross-reference categorization mapping may be aligned to other publicly available industry classification data in the mapping component 301. For example, Bureau of Labor Statistics (BLS), financial stock markets basket indices could be used. In particular, stock baskets could be very dynamic and the composition of any particular stock fluctuates from stock market to stock market. Additionally, mutual fund indices are similarly as dynamic (for example, sector stocksâtransportation, consumer, entertainment). As such, sampling module 303, data collection module 305, time series adjustment module 307, transformation and data modeling module 309, and a performance tracking module 311 could be implemented under the teaches of the present disclosure.
One or more aspects of the disclosure may be embodied in computer-usable data or computer-executable instructions, such as in one or more program modules, executed by one or more computers or other devices to perform the operations described herein. Generally, program modules include routines, programs, objects, components, data structures, and the like that perform particular tasks or implement particular abstract data types when executed by one or more processors in a computer or other data processing device. The computer-executable instructions for system 300 may be stored on a computer-readable medium such as a hard disk, optical disk, removable storage media, solid-state memory, RAM, and the like. The functionality of the program modules may be combined or distributed as desired in various embodiments. In addition, the functionality may be embodied in whole or in part in firmware or hardware equivalents, such as integrated circuits, application-specific integrated circuits (ASICs), field programmable gate arrays (FPGA), and the like. Particular data structures may be used to more effectively implement one or more aspects of the disclosure, and such data structures are contemplated to be within the scope of computer executable instructions and computer-usable data described herein.
Various aspects described herein may be embodied as a method, an apparatus, or as one or more computer-readable media storing computer-executable instructions. Accordingly, those aspects may take the form of an entirely hardware embodiment, an entirely software embodiment, an entirely firmware embodiment, or an embodiment combining software, hardware, and firmware aspects in any combination. In addition, various signals representing data or events as described herein may be transferred between a source and a destination in the form of light or electromagnetic waves traveling through signal-conducting media such as metal wires, optical fibers, or wireless transmission media (e.g., air or space). In general, the one or more computer-readable media may comprise one or more non-transitory computer-readable media.
As described herein, the various methods and acts may be operative across one or more computing servers and one or more networks. The functionality may be distributed in any manner, or may be located in a single computing device (e.g., a server, a client computer, and the like). Aspects of the disclosure have been described in terms of illustrative embodiments thereof. Numerous other embodiments, modifications, and variations within the scope and spirit of the appended claims will occur to persons of ordinary skill in the art from a review of this disclosure. For example, one or more of the steps depicted in the illustrative figures may be performed in other than the recited order, and one or more depicted steps may be optional in accordance with aspects of the disclosure.
