Note : Les descriptions sont présentées dans la langue officielle dans laquelle elles ont été soumises.
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SYSTEMS AND METHODS FOR GENERATING INDUSTRY OUTLOOK SCORES
BACKGROUND
[0001] The present invention relates to systems and methods for the
generation of
industry outlook scores. These outlook scores enable effortless and improved
insight into the
current and future state of industries. These metrics are very useful to
business decision
makers, investors and operations experts.
[0002] Many factors influence the success or failure of a business or
other
organization. Many of these factors include controllable variables, such as
product
development, talent acquisition and retention, and securing business deals.
However, a
significant amount of the variables influencing a business' success are
external to the
organization. These external factors that influence an organization are
typically entirely out
of control of the organization, and are often poorly understood or accounted
for during
business planning. Generally, one of the most difficult variables for a
business to account for
is the general health of a given business sector.
[0003] While these external factors are not necessarily able to be
altered, being able
to incorporate them into business planning allows a business to better
understand the impact
on the business, and make strategic decisions that take into account these
external factors.
This may result in improved business performance, investing decisions, and
operational
efficiency. However, it has traditionally been very difficult to properly
account for, or model,
these external factors; let alone generate meaningful forecasts using many
different factors in
a statistically meaningful and user friendly way.
[0004] For example, many industry outlooks that current exist are merely
opinions of
so-called "experts" that may identify one or two factors that impact the
industry. While these
expert forecasts of industry health have value, they provide a very limited,
and often
inaccurate, perspective into the industry. Further these forecasts are
generally provided in a
qualitative format, rather than as a quantitative measure. For example, the
housing industry
may be considered "healthy" if the prior year demand was strong and the number
of housing
starts is up. However, the degree of 'health' in the market versus a prior
period is not
necessarily available of well defined.
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[0005] As a result, current analytical methods are incomplete, not
quantitative, time
consuming and labor intensive processes that are inadequate for today's
competitive,
complex and constantly evolving business landscape.
[0006] It is therefore apparent that an urgent need exists for
organizational solutions
that enable the generation of industry outlook scores. These systems and
methods for
generating industry outlook scores enables better business and investment
functioning.
SUMMARY
[0007] To achieve the foregoing and in accordance with the present
invention,
systems and methods for generating industry outlook scores are provided. Such
systems and
methods enable business persons, investors, and industry strategists to better
understand the
present state of their industries, and more importantly, to have foresight
into the future state
of their industry.
[0008] In some embodiments, the initial step is to isolate the datasets
that are factors
for the industry being scored. These datasets are then normalized by smoothing
volatility
from the elected datasets, aligning the datasets by similar dates, classifying
the datasets as
normal, inverted or diffusion, determining month-to-month change of each
dataset based
upon the classification, and adjusting to equalize volatility between
datasets. The smoothing
volatility from the elected datasets may utilize a Hodrick Prescott filter.
The normal
classified datasets are procyclic to the index, the inverse classified
datasets are counter-cyclic
to the index, and the diffusion classified datasets are measures of the
proportion of the dataset
that are positive impacts on the index.
[0009] Subsequently the outlook score can be generated by generating a
growth rate
index, summing the growth rates to equate trends to a coincidence index,
computing an index
with a symmetric percent change formula, rebasing the index to average 100,
converting the
index to a three period year over year percent change, and converting the
three period year
over year percent change to a normalized scale. Converting the three period
year over year
percent change to the normalized scale includes setting the minimum value of
the three
period year over year percent change to zero and the maximum value of the
three period year
over year percent change to 1000 on a linear scale.
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[0010] Lastly, the resulting outlook score may be characterized, compared
to prior
scores to identify trends and be displayed to the user. The characterization
may include
grouping scores into quartiles and color coding the scores accordingly.
[0011] Note that the various features of the present invention described
above may be
practiced alone or in combination. These and other features of the present
invention will be
described in more detail below in the detailed description of the invention
and in conjunction
with the following figures.
BRIEF DESCRIPTION OF THE DRAWINGS
[0012] In order that the present invention may be more clearly
ascertained, some
embodiments will now be described, by way of example, with reference to the
accompanying
drawings, in which:
[0013] Figure 1A is an example logical diagram of a data management
system for
generating industry outlook scores, in accordance with some embodiments;
[0014] Figure 1B is a second example logical diagram of a data management
system
for generating industry outlook scores, in accordance with some embodiments;
[0015] Figure 2 is an example logical diagram of an application server,
in accordance
with some embodiments;
[0016] Figure 3 is a flow chart diagram of an example high level process
for
forecasting utilizing time series datasets, in accordance with some
embodiments;
[0017] Figure 4 is a flow chart diagram of an example high level process
for the
generation of composites, in accordance with some embodiments;
[0018] Figure 5A-C are flow chart diagrams of an example processes for
the
generation of the forecasts, in accordance with some embodiments;
[0019] Figure 6 is a flow chart diagram of an example process for the
analysis of the
forecasts, in accordance with some embodiments;
[0020] Figure 7 is a flow chart diagram of an example process for the
generation of
industry outlook scores, in accordance with some embodiments;
[0021] Figures 8-10 are example screenshots illustrating the industry
outlook score
interfaces, in accordance with some embodiments; and
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[0022] Figures 11A and 11B illustrate exemplary computer systems capable
of
implementing embodiments of the data management and forecasting system.
DETAILED DESCRIPTION
[0023] The present invention will now be described in detail with
reference to several
embodiments thereof as illustrated in the accompanying drawings. In the
following
description, numerous specific details are set forth in order to provide a
thorough
understanding of embodiments of the present invention. It will be apparent,
however, to one
skilled in the art, that embodiments may be practiced without some or all of
these specific
details. In other instances, well known process steps and/or structures have
not been
described in detail in order to not unnecessarily obscure the present
invention. The features
and advantages of embodiments may be better understood with reference to the
drawings and
discussions that follow.
[0024] Aspects, features and advantages of exemplary embodiments of the
present
invention will become better understood with regard to the following
description in
connection with the accompanying drawing(s). It should be apparent to those
skilled in the
art that the described embodiments of the present invention provided herein
are illustrative
only and not limiting, having been presented by way of example only. All
features disclosed
in this description may be replaced by alternative features serving the same
or similar
purpose, unless expressly stated otherwise. Therefore, numerous other
embodiments of the
modifications thereof are contemplated as falling within the scope of the
present invention as
defined herein and equivalents thereto. Hence, use of absolute and/or
sequential terms, such
as, for example, "will," "will not," "shall," "shall not," "must," "must not,"
"only," "first,"
"initially," "next," "subsequently," "before," "after," "lastly," and
"finally," are not meant to
limit the scope of the present invention as the embodiments disclosed herein
are merely
exemplary.
[0025] Note that significant portions of this disclosure will focus on
the generation of
industry outlook scores for businesses. While this is intended as a common use
case, it
should be understood that the presently disclosed systems and methods are
useful for the
generation of any industry outlook scores based upon any time series data
sets, for
consumption by any kind of user. For example, the presently disclosed systems
and methods
could be relied upon by a researcher to predict trends as easily as it is used
by a business to
forecast sales trends. As such, any time the term 'business' is used in the
context of this
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disclosure it should be understood that this may extend to any organization
type: individual,
investor group, business entity, governmental group, non-profit, religious
affiliation, research
institution, and the like. Further, references to an industry outlook score
should be
understood to not be limited to commerce, but rather to any situation where an
outlook score
may be needed or desired.
[0026] Lastly, note that the following description will be provided in a
series of
subsections for clarification purposes. These following subsections are not
intended to
artificially limit the scope of the disclosure, and as such any portion of one
section should be
understood to apply, if desired, to another section.
I. DATA MANAGEMENT SYSTEMS FOR GENERATION OF INDUSTRY OUTLOOK
SCORES
[0027] The present invention relates to systems and methods for using
available data
and metrics to generate an entirely new data set through transformations to
yield industry
outlook scores. While various indices are already known, the presently
disclosed systems
and methods provide a score that is forward looking rather than providing
merely a snapshot
of the current situation. Further, the industry outlook scores are generated
in such a fashion
that the score value is normalized regardless of what industry is being
compared. Thus a
score of 600 for business to business (B2B) industry sector would indicate the
same degree of
health as a score of 600 in the construction sector, despite the very
different underlying data.
Such systems and methods allow for superior insight into current and near
future health of a
given industry sector. This enables for better business planning, preparation,
investment, and
generally may assist in influencing behaviors in more profitable ways.
[0028] To facilitate discussion, Figure 1A is an example logical diagram of
a data
management system for generation of industry outlook scores 100. The data
analysis system
100 connects a given analyst user 105 through a network 110 to the system
application server
115. A database 120 (or other suitable dataset based upon forecast sought) is
linked to the
system application server via connection 121 and the database 120 thus
provides access to the
data necessary for utilization by the application server 115.
[0029] The database 120 is populated with data delivered by and through the
data
aggregation server 125 via connection 126. Data aggregation server 125 is
configured to have
access to a number of data sources, for instance external data sources 130
through connection
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131. The data aggregation server can also be configured to have access to
proprietary or
internal data sources, i.e. customer data sources 132, through connection 133.
The
aggregated data may be stored in a relational database (RDBM) or in big data-
related storage
facilities (e.g., Hadoop, NoSQL), with its formatting pre-processed to some
degree (if
desired) to conform to the data format requirement of the analysis component.
[0030] Network 110 provides access to the user or data analyst (the user
analyst).
User analyst 105 will typically access the system through an internet browser,
such as
Mozilla Firefox, or a standalone application, such as an app on tablet 151. As
such, the user
analyst (as shown by arrow 135) may use an internet connected device such as
browser
terminal 150, whether a personal computer, mainframe computer, or VT100
emulating
terminal. Alternatively, mobile devices such as a tablet computer 151, smart
telephone, or
wirelessly connected laptop, whether operated over the internet or other
digital
telecommunications networks, such as a 3G network. In any implementation, a
data
connection 140 is established between the terminal (i.e., 150 or 151) through
network 110 to
the application server 115 through connection 116.
[0031] Network 110 is depicted as a network cloud and as such is
representative of a
wide variety of telecommunications networks, for instance the world wide web,
the internet,
secure data networks, such as those provided by financial institutions or
government entities
such as the Department of Treasury or Department of Commerce, internal
networks such as
local Ethernet networks or intranets, direct connections by fiber optic
networks, analog
telephone networks, through satellite transmission, or through any combination
thereof
[0032] The database 120 serves as an online available database repository
for
collected data including such data as internal metrics. Internal metrics can
be comprised of,
for instance, company financial data of a company or other entity, or data
derived from
proprietary subscription sources. Economic, demographic, and statistical data
that are
collected from various sources and stored in a relational database, may reside
in a local
hardware set or within a company intranet, or may be hosted and maintained by
a third-party
and made accessible via the internet.
[0033] The application server 115 provides access to a system that
provides a set of
calculations based on system formula used to calculate the leading, lagging,
coincident,
procyclic, acyclic, and counter-cyclic nature of economic, demographic, or
statistical data
compared to internal metrics, i.e., company financial results, or other
external metrics. The
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system also provides for formula that may be used to calculate a plurality of
industry outlook
scores based on projected or actual economic, demographic, and statistical
data and company
financial or sold volume or quantity data. Details of the formulas and
processes utilized for
the calculation of these industry outlook scores shall be provided in further
detail below.
These calculations can be displayed by the system in chart or other graphical
format.
[0034] In some embodiments, changes observed in a metric may also be
classified
according to its direction of change relative to the indicator that it is
being measured against.
When the metric changes in the same direction as the indicator, the
relationship is said to be
`procyclic'. When the change is in the opposite direction as the indicator,
the relationship is
said to be 'countercyclic'. Because it is rare that any two metrics will be
fully procyclic or
countercyclic, it is also possible that a metric and an indicator can be
acyclic--i.e., the metric
exhibits both procyclic and countercyclic movement with respect to the
indicator.
[0035] The application residing on server 115 is provided access to
interact with the
customer datasource(s) 132 through the database 120 to perform automatic
calculations
which identify leading, lagging, and coincident indicators as well as the
procyclic, acyclic,
and counter-cyclic relationships between customer data and the available
economic,
demographic, and statistical data. Additionally, the industry outlook scores
may be
automatically populated on a periodic schedule, i.e. every month. Users 105 of
the software
applications that can be made available on the application server 115 are able
to select and
view charts or monitor dashboard modules displaying the results of the
calculations
performed by the system. In some embodiments, user 105 can select data in the
customer
repository for use in the calculations that may allow the user to forecast
future performance,
or tune the industry outlook scores. The types of indicators and internal data
are discussed in
more detail in connection with the discourse accompanying the following
figures.
Alternatively, users can view external economic, demographic, and statistical
data only and
do not have to interface with internal results, at the option of the user. In
yet other
embodiments, all internal and external data may be shielded from the user, and
only the
resulting industry outlook scores is provided to the user for ease of use.
[0036] Data is collected for external indicators and internal metrics of
a company
through the data aggregation server 125. The formulas built into the
application identify
relationships between the data. Users 105 can then use the charting components
to view the
results of the calculations and industry outlook scores. In some embodiments,
the data can be
entered into the database 120 manually, as opposed to utilizing the data
aggregation server
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125 and interface for calculation and forecasting. In some embodiments, the
users 105 can
enter and view any type of data and use the applications to view charts and
graphs of the data.
[0037] Alternatively, in some system users may have sensitive data that
requires it to
be maintained within the corporate environment. Figure 1B depicts components
of the system
in an exemplary configuration to achieve enhanced data security and internal
accessibility
while maintaining the usefulness of the system and methods disclosed herein.
For example,
the data management system 101 may be configured in such a manner so that the
application
and aggregation server functions described in connection with Figure 1A are
provided by one
or more internal application/aggregation servers 160. The internal server 160
access external
data sources 180 through metrics database 190, which may have its own
aggregation
implementation as well. The internal server accesses the metrics database 190
through the
web or other such network 110 via connections 162 and 192. The metrics
database 190
acquires the appropriate data sets from one or more external sources, as at
180, through
connection 182.
[0038] The one or more customer data sources 170 may be continue to be
housed
internally and securely within the internal network. The internal server 160
access the various
internal sources 170 via connection 172, and implements the same type of
aggregation
techniques described above. The user 105 of the system then accesses the
application server
160 with a tablet 151 or other browser software 150 via connections 135 and
140, as in
Figure 1A. External data sources 130 and 180 may be commercial data
subscriptions, public
data sources, or data entered into an accessible form manually.
[0039] Figure 2 is an example logical diagram of an application server
160 that
includes various subcomponents that act in concert to enable a number of
functions, including
the generation of composites, forecasts and, central to this disclosure,
industry outlook scores.
Generally the data being leveraged for the generation of industry outlook
scores includes
economic, demographic, geopolitical, public record and statistical data. In
some
embodiments, the system utilizes any time series dataset. This time series
data stored in the
metrics database 120, is available to all subsystems of the application server
160 for
manipulation, transformation, aggregation, and analysis.
[0040] The subcomponents of the application server 160 are illustrated as
unique
modules within the server coupled by a common bus. While this embodiment is
useful for
clarification purposes, it should be understood that the presently discussed
application server
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may consist of logical subcomponents operating within a single or distributed
computing
architecture, may include individual and dedicated hardware for each of the
enumerated
subcomponents, may include hard coded firmware devices within a server
architecture, or
any permutation of the embodiments described above. Further, it should be
understood that
the listed subcomponents are not an exhaustive listing of the functionality of
the application
server 160, and as such more or fewer than the listed subcomponents could
exist in any given
embodiment of the application server when deployed.
[0041] The application server 160 includes a composite builder 210 that
is capable of
combining various metrics from the metric database 120 (also referred to as
factors or
indicators), and manipulate them in order to generate composite indexes. These
composites
enable are entirely new datasets generated by transforming one or more
existing datasets.
The composite builder 210 also has the ability to assign access controls to
the composites (to
ensure organizational security and protection of intellectual property), and
automatically
update the composites as updated underlying data becomes available. In
addition to
providing useful tools user-friendly interfaces for searching, compiling and
transforming the
indicators, the composite builder 210 may provide suggestions to a user for
inclusion of
particular indicator data and possible manipulations based upon data type and
statistical
measures.
[0042] The application server 160 also includes a forecast builder 220.
The forecast
builder's 220 functionality shall be discussed in considerable details below;
however, at its
root it allows for the advanced compilation of many indicators (including
other published
composite metrics and forecasts) and enables unique manipulation of these
datasets in order
to generate forecasts from any time series datasets. Some of the manipulations
enabled by
the forecast builder are the ability to visualize, on the fly, the R2,
procyclic and countercyclic
values for each indicator compared to the forecast, and further allows for the
locking of any
indicators time domain, and to shift other indicators and automatically update
statistical
measures. Additionally, the forecast builder 220 may provide the user
suggestions of suitable
indicators, and manipulations to indicators to ensure a 'best' fit between
prior actuals and the
forecast over the same time period. The 'best' fit may include a localized
maxima
optimization of weighted statistical measures. For example, the R2, procyclic
and
countercyclic values could each be assigned a multiplier and the time domain
offset used for
any given indicator could be optimized for accordingly. The
multipliers/weights could, in
some embodiments, be user defined.
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[0043] Continuing, the application server 160 also includes an industry
outlook score
generator 230. The industry outlook score generator 230 is essentially a
specialized
composite builder that is not subject to the user manipulation that the
composite builder 210
includes. The reason for this limitation of user customization is to maintain
the normalization
between the scores generated between the various industry sectors. As
previously noted, a
score of a given number in one industry can be directly compared to the
numerical score in
another industry sector. Despite the very different underlying data sources,
and differences in
the industries themselves, the industry outlook scores are dimensionless and
provide a raw
measure of an industries expected health over a relatively short timeframe.
[0044] In some embodiments, the industry outlook scores may range between
0 and
1000, and may indicate the health of the industry over the next six months. In
alternate
embodiments, the industry outlook scores may be normalized for a different
value range,
from 0 to 100 for example. Likewise, the underlying data and weights afforded
to each data
type may be modified to alter the time period over which the industry outlook
score is
providing a measure.
[0045] In some embodiments, the industry outlook scores may be calculated
using a
generic macro equation. In some embodiments, the factors used for the
calculation of the
outlook score are collected and a Hodrick Prescott filter is applied in order
to reduce month-
to-month volatility. The Hodrick Prescott filter may take the form of:
xi Sh. ct
[0046] Where xt is the original series composed of a trend component (gt)
and a
cyclical component (ct). The Hodrick Prescott filter isolates the cycle
component by the
following minimization problem:
[0047] The first term of the above equation is a measure of fitness of
the tie series
while the second term is a measure of smoothness. The X, is a "trade off'
parameter for
balancing the fitness to smoothness. At X, being zero, the trend is equivalent
to the original
series, and as it increases the trend approaches linear. In some embodiments,
a factor of 50,
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75, 100, 125, 150 or 175 is utilized for the term X,. It should be understood
that other
volatility reduction techniques may be employed in alternate embodiments.
[0048] After smoothing the datasets, they may be all aligned by date.
Next each
indicator is assigned an identifier. These identifiers include a "normal"
identification for pro-
cyclic indicators, an "inverse" identification for counter cyclic indicators,
and "diffusion"
identification for indicators that are diffusion indexes. Diffusion indexes
measure the
proportion of the components that contribute positively to the index.
Components are each
sorted by how much they change, and are assigned a value accordingly. In some
embodiments, components that rise more than 0.05 percent are given a value of
1,
components that change less than 0.05 percent are given a value of 0.5, and
components that
fall more than 0.05 percent are given a value of 0. The value of the
components is summed,
divided by the total number of components (averaged) and multiplied by 100 to
result in a
percentage.
[0049] After applying identifiers to the components, the month-to-month
change for
each component is computed. For a 'normal' component x, this calculation may
take the
form of:
¨
+
[0050] For an 'inverse' component x, this equation may take the form of:
+ x)
[0051] Lastly, for a 'diffusion' component the monthly level is used for
the month-to-
month change as these indexes are already normalized by subtracting their
sample mean and
dividing by their standard deviation.
[0052] After computing the month-to-month changes, the standard deviation
I), of the
changes for each component are calculated. The standard deviation is inverted
v.1- and the
k y-1:¨
.)õõõAõ:
sum k is calculated by:
[0053] The sum is restated so that the index's component standardization
factors sum
rk = x- ¨
to one, as shown here: g . The
adjusted contribution mt in each component is the
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monthly contribution multiplied by the corresponding component standardization
factor, as
illustrated in this equation:
[0054] The adjusted contribution mt is added across all the components
for each
month to obtain a growth rate it of the index, as shown by: . The sum of
the
growth rates for all the components of the outlook score are then adjusted to
equate their
trends to that of the coincidence index. This is accomplished by applying an
adjustment
factor a to the growth rates of the index each month, as shown: it; Lt. + .
[0055] Subsequently, the index level is computed using a symmetric
percent change
formula. This computation may include a recursive calculation starting from an
initial value
of 100 for the first month of the sample period, such that the value is
calculated as:
200 + 4+1
-
[0056] Next the index is multiplied by 100 and divided by the average
value of the
twelve months of the based year. Then the index is converted to a three period
year over year
percent change value. This is calculated by calculating a three month rolling
sum of the
above calculated index divided by the same period one year prior.
[0057] Lastly, the growth rate is converted to the appropriate scale. In
some
embodiment, this includes converting to a 0-1000 point scale. This may be
achieved by a
simple linear equation where the minimum growth rate is equivalent to 0 and
the maximum
rate is equivalent to 1000.
[0058] The industries for which an outlook score may include, by way of
example,
automotive sales, business to business (B2B) services, business to consumer
(B2C) services,
chemical manufacture, construction of non-residential structures, construction
of residential
structures, industrial production, restaurants, retail, steel,
telecommunications, healthcare,
hospitality, tourism, durable goods manufacturing, and the like. It should be
understood that
this is not by any means an exhaustive listing of the various industry
segments for which an
outlook score may be generated. Further it should be understood that any of
these industries
may be further sub-segmented by region, category or brand, in some
embodiments. For
example, the auto sales industry may be refined to illustrate only sales of
light trucks in the
northeast of the US for a particular user.
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[0059] The factors and underlying data utilized to generate each of the
outlook scores
may vary considerably, in some embodiments, based upon the industry segment.
For
example, for the automotive industry sector, the factors utilized to generate
the outlook score
may include residential architectural billings index, consumer sentiment
scores, ISM
manufacturing index of new orders, Moody's Seasoned Aaa Corporate bond yield,
personal
savings rate and consumer price index for urban consumers. In contrast, B2B
service sector
may rely upon commercial architectural billings index, Cass Freight index of
expenditures,
economic policy uncertainty index for the United States, NFIB small business
optimism
index, United States Non-Manufacturing Business Tendency Survey: Business
Situation and
Activity, and an adjusted S&P 500 score. The factors for B2C services may
include ISM
manufacturing index of new orders, personal savings rate and consumer
confidence index.
Chemicals industry sectors may depend upon industrial production and capacity
utilization
rate for chemicals, architectural billings index for new projects inquiries,
ISM manufacturing
index of new orders, real average hourly earnings, producer price index for
chemical
manufacturing, an adjusted materials select sector index, S&P Case-Shiller 10-
City home
price sales pair count, and the average weekly hours of production employees
in the chemical
sector. For the consumer packaged goods sector, the factors relied upon
include ISM PMI
composite, consumer sentiment, an adjusted J&J stock price, and the S&P Case-
Shiller 10-
City home sales arima 2. For the outlook for the GDP, the factors relied upon
include
Prevedere retail leading indicator composite, Prevedere industrial production
leading
indicator composite, Prevedere residential construction leading indicator
composite, NFIB
small business optimism index, Bank of America Merrill Lynch US corporate AAA
option
adjusted spread, and ISM manufacturing PMI composite index. For the outlook of
industrial
production, the factors relied upon include ISM manufacturing PMI composite
index,
architectural billings index for new projects inquiries, consumer sentiment
scores, real
personal consumption expenditures for durable goods, and an adjusted score of
American
Express Company stock price. For the outlook of non-residential construction,
the factors
relied upon include value of manufacturers' new orders for durable goods for
the electrical
equipment industry, total business sales, commercial paper outstanding, and
construction
employment. For the outlook for residential construction, the factors relied
upon may include
S&P Case-Shiller 20-City home price sales pair count, new homes sold in the
United States,
consumer sentiment, assets and liabilities of commercial banks in the United
States, forecasts
of non-farm job openings, and agricultural billings index for residential. The
outlook for the
restaurant sector may rely upon factors such as real disposable personal
income, food service
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spread, Prevedere retail leading indicator composite, and adjusted consumer
discrete select
sector SPDR. For the retail industry outlook the factors that may be relied
upon may include
personal savings rates, consumer sentiment, the S&P 500, a volatility measure
of the S&P
500, agricultural billings index for residential, ISM manufacturing index of
new orders, and
real average hourly earnings. For the steel industry outlook score, the
factors relied upon
may include ISM manufacturing index of new orders, non-branch merchant
wholesalers
durable goods inventory to sales ratio, architectural billings index for new
projects inquiries,
an adjusted United States Steel Corporation stock price, and the value of
manufacturers' new
orders for durable goods for iron and steel mills. For the telecom industry
outlook score, the
factors relied upon may include the Prevedere industrial production leading
indicator
composite, personal savings rate, and the value of manufacturers' new orders
for the
communication equipment industries.
[0060] Returning to Figure 2, the application server 160 also includes an
access
controller 240 to protect various data from improper access. Even within an
organization, it
may be desirable for various employees or agents to have split access to
various sensitive
data sources, forecasts or models. Further, within a service or consulting
organization, it is
very important to separate various clients' data, and role access control
enables this data from
being improperly comingled.
[0061] An add-in manager 250 provides add-in application interfaces
(APIs), emails,
XLS and/or via subscriptions in order to export data for various external
systems. For
example the system may include Microsoft Excel , SAP and similar extensions
for
outputting raw data sets, forecast calculations and models.
[0062] Lastly, a publisher 260 allows for the composites generated by the
composite
builder 210, and forecasts generated via the forecast builder 220 and the
outlook scores
generated by the industry outlook score generator to be published, with
appropriate access
controls, for visualization and manipulation by the users.
[0063] By automating an otherwise time-consuming and labor-intensive
process, the
above-described data management system for generating industry outlook scores
offers many
advantages, including the normalization of a score that may be utilized to
compare industries
current condition, forward looking condition, and the ability to directly
compare the condition
of different industry types. In addition, the application server no longer
requires user
expertise. The result is substantially reduced user effort needed for the
generation of timely
and accurate outlook scores.
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[0064] Now that the systems for data management for generating industry
outlook
scores have been described in considerable detail, attention will be turned
towards methods of
operation in the following subsection.
DATA MANAGEMENT AND OUTLOOK SCORE GENERATION METHODS
[0065] To facilitate the discussion, a series of flowcharts are provided.
Figures 3-6
provide an overview of the composite building and forecast processes. Figure 7
explores
outlook score generation in greater detail. Fundamentally, outlook score
generation is the
production of a series of specialized composites and forecasts that provide
for a normalized
score across different industries, and a common time horizon for the forecast.
Unlike the
generic composite and forecasts discussed in Figures 3-6, these outlook scores
are not subject
to the same degree of user manipulation in order to maintain their
functionality as comparable
across industry segments and for a known time horizon.
[0066] Figure 3 is a flow chart diagram of an example high level process
300 for
forecasting utilizing time series datasets. In this example process, the user
of the system
initially logs in (at 310) using a user name and password combination,
biometric identifier,
physical or software key, or other suitable method for accessing the system
with a defined
user account. The user account enables proper access control to datasets to
ensure that data is
protected within an organization and between organizations.
[0067] The user role access is confirmed (at 320) and the user is able to
search and
manipulate appropriate datasets. This allows the user to generate composites
(at 330) for
enhanced analysis. As previously discussed, a composite is an entirely new
dataset generated
via the compilation, transformation and aggregation of existing indicator data
sets.
[0068] Figure 4 provides a more detailed example high level process for
the
generation of composites. For composite generation, the user initially selects
a dataset to be
utilized (at 410). This selection may employ the user searching for a specific
dataset using a
keyword search. The datasets matching the keyword may be presented to the user
for
selection. In some embodiments, the search results may be ordered by best
match to the
keyword. In other embodiments, the search results may be ordered by alternate
metrics, such
as popularity of a given indicator (used in many other forecast models),
accuracy of indicator
data, frequency of indicator data being updated, or 'fit' between the
indicator and the
composite. Search results may further be sorted and filtered by certain
characteristics of the
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data series, for instance, by region, industry, category, attribute, or the
like. In some cases,
search display may depend upon a weighted algorithm of any combination of the
above
factors.
[0069] A 'fit' between the composite and the indicator may be measured by
the R2,
procyclic and/or countercyclic value when comparing the indicator to the
composite. For
example, if the composite is for domestic construction spend futures,
indicators with a high
degree of 'fit' may include stock prices for home improvement companies,
number of
building permit starts reported by the government, and raw material costs for
concrete,
lumber and steel, for example.
[0070] In addition to utilizing all or some of the above factors for
displaying search
results, some embodiments of the method may generate suggestions for
indicators to the user
independent of the search feature. Likewise, when a user selects an indicator,
the system may
be able to provide alternate recommendations of 'better' indicators based on
any of the above
factors.
[0071] Regardless of if an indicator is selected via a suggestion or a
search, the next
step is to normalize the datasets (at 420). This may include transforming all
the datasets into
a percent change over a given time period, an absolute dollar amount over a
defined time
period, or the like. Likewise, periods of time may also be normalized, such
that the analysis
window for all factors is equal. Next the user is able to configure a formula
that takes each
indicator and allows them to be combined (at 430). In some embodiments, this
formula is
freeform, allowing the user to tailor the formula however desired. In
alternate embodiments,
the formula configuration includes a set of discrete transformations,
including providing each
indicator with a weight, and allowing the indicators to be added/subtracted
and/or multiplied
or divided against any other single or group of indicators.
[0072] Once the formula has been configured, the system calculates the
composite (at
440) and waits for a change in the underlying datasets (at 450). At any time
the composite
may be output for usage by another tool, such as a forecast (at 470), but upon
a change to one
of the indicators that comprises the composite, the method may cause a real-
time update of
the composite calculation (at 460). Any downstream tool the composite has been
incorporated into will likewise receive an update.
[0073] Returning to Figure 3, once composites have been generated, the
method
determines if it is desirable to publish the composite as an indicator (at
340) within the model
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library (as previously discussed). If so, then the composite is published (at
350) with
appropriate access controls. Any access controls applied to the underlying
datasets are
automatically applied to the composite, in some embodiments, and further
access controls
may be enforced by the composite author as well.
[0074] Next, a forecast may be generated (at 360), which is described in
considerably
more detail in reference to Figures 5A-5C. At Figure 5A, the forecast
generation process 360
initially begins with the selection of an indicator (at 510). This selection
process may include
searches or suggestions of indicators in much the same manner as described
above in relation
to the building of a composite. Again, the suggestion of an indicator (or
display or search
results, depending upon embodiment), may be driven by popularity of a given
indicator,
accuracy of indicator data, frequency of indicator data being updated, or
'fit' between the
indicator and the forecast.
[0075] After the indicator has been selected, the system performs a check
on whether
the selected indicator is relative to the forecast (at 520). This step enables
data that loses
granularity, or becomes less accurate, upon transformation for the forecast,
to be identified
and either replaced or weeded out. For example, in some cases a set of revenue
data may be
needed to be converted into a year-over-year indicator. This aggregation may
cause an
artificial suppression of the indicator's value, and thus negatively impact
the forecast. Such
data is deemed not relative, and the method looks for whether raw data is
available for the
metric being sought (at 530). For example, maybe there is a metric for such
year-over-year
measure, or other revenue data of sufficient frequency that the system could
generate such
data without a loss of accuracy. If so, or if the original indicator selected
is relative, then the
method may forecast using the appropriate data (at 540). Otherwise, the method
may outright
reject the indicator as being included in the forecast (at 550). This may
include an error
message provided to the user explaining why the dataset is improper for the
forecast.
[0076] This entire process may be repeated for additional indicators if
they are
present (at 560). This allows for forecasts that include as many indicators as
a user desires.
Once all indicators are selected, however, the method continues with the
selection of
parameters for the forecast (at 570). Figure 5B provides more details
regarding this example
process 570 for selection of forecast parameters. Initially the forecast type
is selected by the
user (at 571). Forecast type may include segmented multivariate forecast,
linear regression
models, piecewise linear models, or the like. Additionally, the calculation
type may be
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selected (at 572). Calculation types include year-over-year percent changes,
month-over-
month, three month moving averages, actual values, and the like.
[0077] Next the user selects the cutoff period for the forecast (at 573).
Typically this
is a time period in the future that provides the user with useful insight into
business decisions,
or other actions, that are to be taken in the near future. Many forecasts
perform very well for
some limited period of time, but then rapidly degrade. These forecast models,
when viewed
in the aggregate, are seen as very poor predictors. However, when subject to a
cutoff period,
these models may in fact be extremely high performing over the time period of
concern. For
this reason, the cutoff period is initially set in order to select the best
forecast parameters and
indicators over the period of interest.
[0078] Next pre-adjustment factors and post-adjustment factors are set (at
574 and
575, respectively). These factors are multipliers to the forecast and/or
indicators that account
for some anomaly in the data. For example, a major snowstorm impacting the
eastern
seaboard may have an exaggerated impact upon heating costs in the region. If
the forecast is
for global demand for heating oil, this unusual event may skew the final
forecast. An
adjustment factor may be leveraged in order to correct for such events.
[0079] Next, for each indicator, a weight and a time offset is provided
(at 576 and
577, respectively). The weight may be any positive or negative number, and is
a multiplier
against the indicator to vary the influence of the indicator in the final
model. A negative
weight will reverse procyclic and countercyclic indicators. Determining
whether an indicator
relationship exists between two data series, as well as the nature and
characteristics of such a
relationship, if found, can be a very valuable tool. Armed with the knowledge,
for example,
that certain macroeconomic metrics are predictors of future internal metrics,
business leaders
can adjust internal processes and goals to increase productivity,
profitability, and
predictability. The time offset allows the user to move the time domain of any
indicator
relevant to the forecast. For example, in the above example of global heating
oil, the global
temperature may have a thirty day lag in reflecting in heating oil prices. In
contrast, refining
capacity versus crude supply may be a leading indicator of the heating oil
prices. These two
example indicators would be given different time offsets in order to refine
the forecast.
[0080] For any forecast indicator, an R2 value, procyclic value and
countercyclic
value is generated in real time for any given weight and time offset. These
statistical
measures enable the user to tailor their model according to their concerns. In
some
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embodiments the weights and offsets for the indicators may be auto-populated
by the method
with suggested values. These values, as previously touched upon, may employ an
optimization algorithm of weighted statistical measures. In some embodiment,
the R2 value,
procyclic value and countercyclic values may be weighted and combined, and
maximum
value generated by a specific weight and offset can be suggested.
[0081] Returning to Figure 5A, after the parameters have been set, the
forecast is
actually calculated (at 580). Figure 5C details this example process 580 for
calculating the
forecast. Initially the indicators are transformed (at 581) according to the
previously defined
parameters. For example the indicator may be transformed into a common format
such as
year-over-year percent change. Next the percent change is determined for each
date based
upon the transformed indicators (at 582), and the percent change is arranged
over the set
period (at 583) defined by the cutoff period. Lastly, the previous year's
value is multiplied
by this percent change for each given date to generate the forward forecast
(at 584). Forward
forecasted indicators may then be weighted and offset according to the defined
parameters.
The forecasted indicators may also be summed and have the pre and post
adjustments applied
in order to generate the final forecast value.
[0082] Returning to Figure 3, after the forecast has been generated, the
forecast is
subsequently analyzed (at 370). The process continues by determining if the
forecast is to be
published as an indicator. As previously mentioned, the published indicators
may be access
controlled for particular users, and may be incorporated into further
forecasts.
[0083] Figure 6 provides further details regarding the example process
370 for the
analysis of the forecasts. For the analysis, initially the forecast is charted
overlying each
indicator value (at 610). This charting allows a user to rapidly ascertain,
using visual cues,
the relationship between the forecast and each given metric. Humans are very
visual, and
being able to graphically identify trends is often much easier than using
numerical data sets.
In addition to the graphs, the R2, procyclic values, and countercyclic values
may be presented
(at 620) alongside the charted indicators.
[0084] Where the current method is particularly potent is its ability to
rapidly shift the
time domains, on the fly, of any of the indicators to determine the impact
this has on the
forecast. In some embodiments, one or more time domain outer bound drag bars
may be
utilized to alter the time domain of indicators. The time domain defining drag
bar may be
graphically manipulated by the user. Moving the drag bar will alter and
redefine the time
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domain in which the selected metrics for a report are displayed. For example,
in one situation
a set of charts could display five metrics for the time period starting
January 2006 and ending
May 2012. By manipulating the drag bar, the time domain and thus the range of
available
data viewed in the report dashboard can be altered. In this example, the
metrics are now
displayed for the time period starting in March 2005 and ending in May 2012.
Note that the
entire time domain defining control may be graphically manipulated along a
line, in some
embodiments, where a lower and upper bound of the time domain are able to be
manipulated,
or the entire range may be merely shifted, thereby maintaining the same range,
or length, of
data represented.
[0085] Unique to the currently disclosed methods, however, is the ability
to lock the
time domain of any given indicator (at 630) such that if an indicator is
locked (at 640) any
changes to the time domain will only shift for non-locked indicators. Upon an
shift in the
time domain, the charts that are locked are kept static (at 650) as the other
graphs are
updated.
[0086] In addition to presenting the graphs comparing indicators to the
forecast, in
some embodiments, the forecast may be displayed versus actual values (for the
past time
period), trends for the forecast are likewise displayed, as well as the future
forecast values (at
660). Forecast horizon, mean absolute percent error, and additional
statistical accuracy
measures for the forecast may also be provided (at 670). Lastly, the eventual
purpose of the
generation of the forecast is to modify user or organization behaviors (at
680).
[0087] Like the composite and forecast generation of Figures 3-6, the
process
disclosed in Figure 7 likewise generates a forecast for a given industry for
the 'health' over a
set future period. This is known as the outlook score for the industry. As
previously noted,
this score may be a single number within a set range, and may indicate the
health of the
industry for a set number of months into the future. In some embodiments this
score may be
a value between 0 and 1000. In some embodiments, this score may be a measure
of industry
health expected in the next six month period.
[0088] By standardizing the range of the outlook scores, and the time
horizon these
scores operate over, the presently disclosed method allows for users to
directly compare
industries that are not related to one another. This may be very useful for
fund managers and
other investors. Likewise, it may provide businesses insights on where to
market and target
resources.
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[0089] The first step in generating and industry outlook score is to
aggregate the
metrics that are employed in the generation of the metric for a given industry
segment (at
710). The components utilized for each industry segment vary based upon which
industry is
being calculated for. As noted above, these components may include other
indexes (such as
the S&P 500), and other metrics (such as consumer sentiment).
[0090] After the pertinent underlying data has been accessed, a transform
is applied to
the data to generate the new outlook score for the industry (at 720). As
previously noted, in
some embodiments, the transform employed may include a number of steps
including a
volatility smoothing procedure, alignment of data by the same dates,
classification of the
components, determining month-to-month changes based upon component
classification,
adjusting to equalize volatility, generating growth rate index, summing the
growth rates to
equate trends to a coincidence index, computing the index with a symmetric
percent change
formula, rebasing the index to average 100, converting the index to a three
period year over
year percent change, and finally converting this to a normalized scale.
[0091] Next the outlook score generated for a given industry segment may
be
bucketed into a 'health' or performance category (at 730). This performance
category may
provide the user with a rapid understanding of the relative performance that
should be
expected from the industry over the following time period of interest. In some
embodiments,
the outlook score is linear, and may be segmented into equal sized 'buckets'
indicating the
industry's outlook. For example, a score between 0 and 250 may be considered
poor,
between 251 and 500 fair, between 501 and 750 good, and between 751 and 1000
excellent.
In other embodiments, more granular classifications may be utilized. In yet
other
embodiments, the score may be non-linear, and the buckets may not be equal
sized. For
example, on a logarithmic scaled outlook score, the buckets could range from 1-
50 for poor,
51-75 for fair, 76-90 for good, and 91-100 for excellent.
[0092] The next step in this example method is to visually distinguish
the score based
upon the 'bucket' it falls into (at 740). Again, the purpose of the outlook
scores is to provide
a user friendly mechanism to readily convey information regarding the health
of an industry
segment over a relatively short time horizon. By visually distinguishing the
score by the
bucket it falls under, the user may rapidly ascertain the general health of
the industry with
very little effort. This visual distinguishing may include any combination of
color
coordination, font selection, display location (such as on a number line style
graphic), font
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sizing, or the like. Examples are provided below of how this visual
distinguishing may be
performed.
[0093] In addition to visually distinguishing the scores, it is also
helpful to users to
understand the shift in the score from the previous month (or however often
the score is
updated in any given embodiment). As such, the method next subtracts the prior
period's
outlook score from the score that has been newly generated (at 750). To yield
a trend value.
The trend value, raw score and bucket visualization may all be provided
graphically to the
user (at 760) to assist in the user's decision making processes, and
ultimately in order to
influence the user's behavior.
[0094] In some embodiments, modifying behaviors may be dependent upon the
user
to formulate and implement. In advanced embodiments, suggested behaviors based
upon the
outlook scores (such as commodity hedging, investment trends, or securing
longer or shorter
term contracts) may be automatically suggested to the user for implementation.
In these
embodiments, the system utilizes rules regarding the user, or organization,
related to
objectives or business goals. These rules/objectives are cross referenced
against the outlook
scores, and advanced machine learning algorithms may be employed in order to
generate the
resulting behavior modification suggestions. In some other embodiments, the
user may
configure state machines in order to leverage outlook scores to generate these
behavior
modification suggestions. Lastly, in even further advanced embodiments, in
addition to the
generation of these suggestions, the system may be further capable of acting
upon the
suggestions autonomously. In some of these embodiments, the user may configure
a set of
rules under which the system is capable of autonomous activity. For example,
the outlook
score may be required to have above a specific accuracy threshold, and the
action may be
limited to a specific dollar amount for example.
EXAMPLES
[0095] Now that the systems and methods for generating industry outlook
scores have
been described in considerable detail, attention will be turned to a series of
example
screenshots of the systems and methods being employed. It should be noted
however, that
these example screenshots are but a limited set of embodiments presented for
clarification
purposes. As such, these example screenshots should not limit the scope of the
presently
disclosed invention in any way.
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[0096] Figure 8 provides a summary screenshot 800 of a series of industry
outlook
scores in a dashboard for exploration by a user. The time period for the
outlook is provided
(at 820) for the user. Each industry is labeled (at 830) and a color
coordinated score is
illustrated (at 840). The change in the score from the last period is likewise
illustrated (at
850) to provide trend context to the user. The score 'buckets' that in this
screenshot include a
color visualization, are enumerated at the bottom of the interface (at 860).
In this example
the scores are broken into four categories: poor, weak, fair and strong.
Alternate numbers,
ranges and names for these score 'buckets' may likewise be employed.
[0097] Note, as previously discussed, the scores are all on a similar
range (from 0-
1000) and are for the same forecast period (here the second quarter of 2016).
This enables
direct comparison between the relative strength of entirely divergent industry
sectors. For
example, construction of non-residential structures is doing fairly well,
whereas industrial
production is doing relatively poorly. For an investor, these numbers could
help determine
which industry sectors to invest in. For a business with many operations, such
information
may help to allocate resources and efforts.
[0098] The user may dig deeper into any of the outlook scores by merely
clicking on
the relevant box. For example, if the user selects the automotive box, a new
page may be
displayed to the user, as seen at Figure 9, with additional details regarding
the outlook score
for the industry of interest, shown generally at 900.
[0099] As with the summary page, the period for which the outlook score
is
forecasting is provided to the user (at 920). The specific industry segment
being looked at is
also identified (at 930). The outlook score is illustrated (at 910). In this
example, the range
of scores is illustrated as a series of color coded bars in a staggered number
line. The outlook
score is illustrated in the color of the bucket it falls into, and is
positioned accordingly in the
number line. Below the number line segment the score falls under is the trend
number for the
score (at 940) along with an explanation of what this may indicate. Again, the
trend is
determined by subtracting the prior outlook score form the current outlook
score. Here the
trend is downward, indicating a softening in the automotive market.
[00100] The buckets, with corresponding color coordination, are
illustrated below the
number line (at 950). Qualitative explanations of what these buckets mean are
likewise
provided. Further, a series of informational explanations are provided below
(at 960). These
explanations may be tailored by the score value, and by the industry segment.
For example,
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in this screenshot, the explanation indicates that this score is a 6 month
leading indicator for
the auto industry. It further explains that the decreasing trend means that
the auto industry
growth will slow over the next two quarters, but that the score is still fair,
suggesting that any
slowed growth is still well insulated from a contraction in the sector.
Lastly, advice is
provided based upon the score.
[00101] In contrast, Figure 10 provides a detailed screenshot of an
industry segment
that is in worse shape than the automotive industry for comparison purposes,
shown generally
at 1000. As with the previous screenshot, the forecast period is illustrated
(at 1020) as well
as the industry name (here B2B services, at 1030). For this segment the
outlook score is
lower, and is positioned along the number line and colored accordingly (at
1010). The trend
number is likewise illustrated (at 1040), as are captions regarding the
buckets (at 1050).
[00102] Significantly, the explanations provided differ from the other
industry outlook
scores due to the differing score value, as well as the differences in the
industry sector (as
seen at 1060). Here the indicator is identified as a 9 month leading
indicator, due in this
example to the accuracy of the forecasts for this industry type. The
explanation of the score
indicates that there is considerable deceleration in this industry segment,
but not necessarily
recessionary conditions.
IV. SYSTEM EMBODIMENTS
[00103] Now that the systems and methods for the generation of industry
outlook
scores have been described, attention shall now be focused upon systems
capable of
executing the above functions. To facilitate this discussion, Figures 11A and
11B illustrate a
Computer System 1100, which is suitable for implementing embodiments of the
present
invention. Figure 11A shows one possible physical form of the Computer System
1100. Of
course, the Computer System 1100 may have many physical forms ranging from a
printed
circuit board, an integrated circuit, and a small handheld device up to a huge
super computer.
Computer system 1100 may include a Monitor 1102, a Display 1104, a Housing
1106, a Disk
Drive 1108, a Keyboard 1110, and a Mouse 1112. Disk 1114 is a computer-
readable medium
used to transfer data to and from Computer System 1100.
[00104] Figure 11B is an example of a block diagram for Computer System
1100.
Attached to System Bus 1120 are a wide variety of subsystems. Processor(s)
1122 (also
referred to as central processing units, or CPUs) are coupled to storage
devices, including
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Memory 1124. Memory 1124 includes random access memory (RAM) and read-only
memory (ROM). As is well known in the art, ROM acts to transfer data and
instructions uni-
directionally to the CPU and RAM is used typically to transfer data and
instructions in a bi-
directional manner. Both of these types of memories may include any suitable
of the
computer-readable media described below. A Fixed Disk 1126 may also be coupled
bi-
directionally to the Processor 1122; it provides additional data storage
capacity and may also
include any of the computer-readable media described below. Fixed Disk 1126
may be used
to store programs, data, and the like and is typically a secondary storage
medium (such as a
hard disk) that is slower than primary storage. It will be appreciated that
the information
retained within Fixed Disk 1126 may, in appropriate cases, be incorporated in
standard
fashion as virtual memory in Memory 1124. Removable Disk 1114 may take the
form of any
of the computer-readable media described below.
[00105] Processor 1122 is also coupled to a variety of input/output
devices, such as
Display 1104, Keyboard 1110, Mouse 1112 and Speakers 1130. In general, an
input/output
device may be any of: video displays, track balls, mice, keyboards,
microphones, touch-
sensitive displays, transducer card readers, magnetic or paper tape readers,
tablets, styluses,
voice or handwriting recognizers, biometrics readers, motion sensors, brain
wave readers, or
other computers. Processor 1122 optionally may be coupled to another computer
or
telecommunications network using Network Interface 1140. With such a Network
Interface
1140, it is contemplated that the Processor 1122 might receive information
from the network,
or might output information to the network in the course of performing the
above-described
generation of industry outlook scores. Furthermore, method embodiments of the
present
invention may execute solely upon Processor 1122 or may execute over a network
such as the
Internet in conjunction with a remote CPU that shares a portion of the
processing.
[00106] Software is typically stored in the non-volatile memory and/or the
drive unit.
Indeed, for large programs, it may not even be possible to store the entire
program in the
memory. Nevertheless, it should be understood that for software to run, if
necessary, it is
moved to a computer readable location appropriate for processing, and for
illustrative
purposes, that location is referred to as the memory in this disclosure. Even
when software is
moved to the memory for execution, the processor will typically make use of
hardware
registers to store values associated with the software, and local cache that,
ideally, serves to
speed up execution. As used herein, a software program is assumed to be stored
at any
known or convenient location (from non-volatile storage to hardware registers)
when the
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software program is referred to as "implemented in a computer-readable
medium." A
processor is considered to be "configured to execute a program" when at least
one value
associated with the program is stored in a register readable by the processor.
[00107] In operation, the computer system 1100 can be controlled by
operating system
software that includes a file management system, such as a disk operating
system. One
example of operating system software with associated file management system
software is
the family of operating systems known as Windows from Microsoft Corporation
of
Redmond, Washington, and their associated file management systems. Another
example of
operating system software with its associated file management system software
is the Linux
operating system and its associated file management system. The file
management system is
typically stored in the non-volatile memory and/or drive unit and causes the
processor to
execute the various acts required by the operating system to input and output
data and to store
data in the memory, including storing files on the non-volatile memory and/or
drive unit.
[00108] Some portions of the detailed description may be presented in
terms of
algorithms and symbolic representations of operations on data bits within a
computer
memory. These algorithmic descriptions and representations are the means used
by those
skilled in the data processing arts to most effectively convey the substance
of their work to
others skilled in the art. An algorithm is, here and generally, conceived to
be a self-consistent
sequence of operations leading to a desired result. The operations are those
requiring
physical manipulations of physical quantities. Usually, though not
necessarily, these
quantities take the form of electrical or magnetic signals capable of being
stored, transferred,
combined, compared, and otherwise manipulated. It has proven convenient at
times,
principally for reasons of common usage, to refer to these signals as bits,
values, elements,
symbols, characters, terms, numbers, or the like.
[00109] The algorithms and displays presented herein are not inherently
related to any
particular computer or other apparatus. Various general purpose systems may be
used with
programs in accordance with the teachings herein, or it may prove convenient
to construct
more specialized apparatus to perform the methods of some embodiments. The
required
structure for a variety of these systems will appear from the description
below. In addition,
the techniques are not described with reference to any particular programming
language, and
various embodiments may, thus, be implemented using a variety of programming
languages.
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[00110] In alternative embodiments, the machine operates as a standalone
device or
may be connected (e.g., networked) to other machines. In a networked
deployment, the
machine may operate in the capacity of a server or a client machine in a
client-server network
environment or as a peer machine in a peer-to-peer (or distributed) network
environment.
[00111] The machine may be a server computer, a client computer, a
personal
computer (PC), a tablet PC, a laptop computer, a set-top box (STB), a personal
digital
assistant (PDA), a cellular telephone, an iPhone, a Blackberry, a processor, a
telephone, a
web appliance, a network router, switch or bridge, or any machine capable of
executing a set
of instructions (sequential or otherwise) that specify actions to be taken by
that machine.
[00112] While the machine-readable medium or machine-readable storage
medium is
shown in an exemplary embodiment to be a single medium, the term "machine-
readable
medium" and "machine-readable storage medium" should be taken to include a
single
medium or multiple media (e.g., a centralized or distributed database, and/or
associated
caches and servers) that store the one or more sets of instructions. The term
"machine-
readable medium" and "machine-readable storage medium" shall also be taken to
include any
medium that is capable of storing, encoding or carrying a set of instructions
for execution by
the machine and that cause the machine to perform any one or more of the
methodologies of
the presently disclosed technique and innovation.
[00113] In general, the routines executed to implement the embodiments of
the
disclosure may be implemented as part of an operating system or a specific
application,
component, program, object, module or sequence of instructions referred to as
"computer
programs." The computer programs typically comprise one or more instructions
set at
various times in various memory and storage devices in a computer, and when
read and
executed by one or more processing units or processors in a computer, cause
the computer to
perform operations to execute elements involving the various aspects of the
disclosure.
[00114] Moreover, while embodiments have been described in the context of
fully
functioning computers and computer systems, those skilled in the art will
appreciate that the
various embodiments are capable of being distributed as a program product in a
variety of
forms, and that the disclosure applies equally regardless of the particular
type of machine or
computer-readable media used to actually effect the distribution
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[00115] While this invention has been described in terms of several
embodiments,
there are alterations, modifications, permutations, and substitute
equivalents, which fall
within the scope of this invention. Although sub-section titles have been
provided to aid in
the description of the invention, these titles are merely illustrative and are
not intended to
limit the scope of the present invention. It should also be noted that there
are many
alternative ways of implementing the methods and apparatuses of the present
invention. It is
therefore intended that the following appended claims be interpreted as
including all such
alterations, modifications, permutations, and substitute equivalents as fall
within the true
spirit and scope of the present invention.
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