CN110110886A - Information forecasting method, device, computer equipment and storage medium - Google Patents
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Abstract
This application involves big data processing field, in particular to a kind of information forecasting method, device, computer equipment and storage medium.The described method includes: obtaining home price beforehand index data, and obtain basic macro-indicators data one by one from base values library；The historic asset relevance data for obtaining each basic macro-indicators data carry out prescreening to basic macro-indicators data according to historic asset relevance data；Calculate the related coefficient of each basic macro-indicators data and home price beforehand index data that pre-sifted is selected；The basic macro-indicators data with home price beforehand index data correlation are filtered out according to calculated related coefficient；The basic macro-indicators data filtered out are analyzed using Principal Component Analysis and construct property market foam index prediction model；The prediction of property market foam index is carried out according to property market foam index prediction model.Information prediction accuracy can be improved using this method.
Description
Technical field
This application involves field of computer technology, more particularly to a kind of information forecasting method, device, computer equipment and
Storage medium.
Background technique
The each of social life is deeply influenced using stock market and property price as the asset price bubbles of main representative
Aspect, becomes the important determinant of national economy sustainable growth and fluctuation, if asset price bubbles cannot be held well
Forming Mechanism, carry out previous precautionary measures, once lather collapse, the strike for macroeconomic will be it is heavy, therefore,
The prediction work of asset price bubbles, which seems, to be even more important.But lacks become to the development of Asset bubble related economic index at present
The effective means of gesture progress Accurate Prediction.
Summary of the invention
Based on this, it is necessary in view of the above technical problems, provide a kind of assets city that can be improved information prediction accuracy
Field foam index prediction technique, device, computer equipment and storage medium.
A kind of property market foam index prediction technique, which comprises
Home price beforehand index data are obtained, and obtain basic macro-indicators data one by one from base values library；
The historic asset relevance data for obtaining each basic macro-indicators data, according to the historic asset relevance
Data carry out prescreening to the basic macro-indicators data；
It is related to the home price beforehand index data to calculate each basic macro-indicators data that pre-sifted is selected
Coefficient；
It is filtered out according to the calculated related coefficient macro with the basis of the home price beforehand index data correlation
See achievement data；
The basic macro-indicators data filtered out are analyzed using Principal Component Analysis and construct property market
Foam index prediction model；
The prediction of property market foam index is carried out according to the property market foam index prediction model.
It is described in one of the embodiments, to be filtered out and the home price elder generation according to the calculated related coefficient
The basic macro-indicators data of row index data correlation, comprising:
Default dependent thresholds are obtained, the corresponding related coefficient being calculated is extracted and is greater than the default dependent thresholds
Basic macro-indicators data；
The Criterion Attribute that obtains the basic macro-indicators data extracted, according to the Criterion Attribute to extracting
The basis macro-indicators data are classified；
The maximum basic macro-indicators data screening of related coefficient corresponding in each classification is come out.
It is described that the maximum basis of related coefficient corresponding in each classification is macro in one of the embodiments,
After sight achievement data screens, further includes:
Asset association label is carried out to the corresponding related coefficient of the basis macro-indicators data filtered out；
Calculated each related coefficient is added to the historic asset relevance number of corresponding basic macro-indicators data
According to.
It is described that the maximum basis of related coefficient corresponding in each classification is macro in one of the embodiments,
After sight achievement data screens, further includes:
The classification quantity of the basic macro-indicators data extracted is obtained, and obtains pre-set level lower numerical limit；
When the classification quantity is less than the pre-set level lower numerical limit, the pre-set level lower numerical limit and institute are calculated
State the difference of classification quantity；
Sequence by the basic macro-indicators data for the extraction not filtered out according to corresponding related coefficient from large to small
It is ranked up, the basic macro-indicators data stood out also is screened, and standing out of filtering out is described
The quantity of basic macro-indicators data is consistent with the difference.
In one of the embodiments, it is described using Principal Component Analysis to the basic macro-indicators data filtered out
It is analyzed and constructs property market foam index prediction model, comprising:
The basic macro-indicators data filtered out are standardized to obtain data matrix；
The covariance matrix of the data matrix is calculated, and calculates the characteristic root and feature vector of the covariance matrix；
Principal component expression formula is determined according to the characteristic root and described eigenvector；
Property market foam index prediction model is constructed according to the principal component expression formula.
It is described in one of the embodiments, to calculate each basic macro-indicators data and the house that pre-sifted is selected
The related coefficient of price beforehand index data, comprising:
Obtain predetermined sequence initial time；
Basic macroscopic view corresponding with the predetermined sequence initial time is extracted from each basic macro-indicators data to refer to
Sequence is marked, and extracts home price corresponding with the predetermined sequence initial time from the home price beforehand index data
Sequence；
Calculate the related coefficient of each basic the macro-indicators sequence and the home price sequence.
A kind of information prediction device, described device include:
Data acquisition module obtains base for obtaining home price beforehand index data, and from base values library one by one
Plinth macro-indicators data；
Pre-screening module, for obtaining the historic asset relevance data of each basic macro-indicators data, according to institute
It states historic asset relevance data and prescreening is carried out to the basic macro-indicators data；
Coefficients calculation block, for calculating each basic macro-indicators data and home price elder generation that pre-sifted is selected
The related coefficient of row index data；
Index screening module, for being filtered out and the home price beforehand index according to the calculated related coefficient
The basic macro-indicators data of data correlation；
Model construction module, for being divided using Principal Component Analysis the basic macro-indicators data filtered out
It analyses and constructs property market foam index prediction model；
Exponential forecasting module, for carrying out property market foam index according to the property market foam index prediction model
Prediction.
Index screening module includes: in one of the embodiments,
Threshold value acquisition submodule extracts the corresponding related coefficient being calculated for obtaining default dependent thresholds
Greater than the basic macro-indicators data of the default dependent thresholds；
Data classification submodule, for obtaining the Criterion Attribute of the basic macro-indicators data extracted, according to institute
Criterion Attribute is stated to classify to the basic macro-indicators data extracted；
Submodule is screened, is used for the maximum basic macro-indicators number of related coefficient corresponding in each classification
According to screening.
A kind of computer equipment, including memory and processor, the memory are stored with computer program, the processing
The step of device realizes the above method when executing the computer program.
A kind of computer readable storage medium, is stored thereon with computer program, and the computer program is held by processor
The step of above method is realized when row.
Above- mentioned information prediction technique, device, computer equipment and storage medium, it is multiple by being obtained from base values library
Basic macro-indicators data, and prescreening, Jin Ergen are carried out to basic macro-indicators data according to historic asset relevance data
Related coefficient between the basic macro-indicators sequence selected according to pre-sifted and home price beforehand index data determines associated
Index, to construct property market foam index prediction model using associated index, the basic macro-indicators of the application are numerous
And abundance, the property market foam index prediction model science of building and reliable, can to property market foam index into
Row Accurate Prediction.
Detailed description of the invention
Fig. 1 is the application scenario diagram of information forecasting method in one embodiment；
Fig. 2 is the flow diagram of information forecasting method in one embodiment；
Fig. 3 is the flow diagram of index screening step in one embodiment；
Fig. 4 is the schematic diagram of the 4 phase property market foam index prediction models constructed in one embodiment；
Fig. 5 is the structural block diagram of information prediction device in one embodiment；
Fig. 6 is the internal structure chart of computer equipment in one embodiment.
Specific embodiment
It is with reference to the accompanying drawings and embodiments, right in order to which the objects, technical solutions and advantages of the application are more clearly understood
The application is further elaborated.It should be appreciated that specific embodiment described herein is only used to paraphrase the application, not
For limiting the application.
Information forecasting method provided by the present application can be applied in application environment as shown in Figure 1.Wherein, terminal 102
It is communicated by network with server 104.Terminal 102 can send the prediction of property market foam index to server 104 and ask
It asks, after server 104 receives request, obtains home price beforehand index data, and obtain basis one by one from base values library
Macro-indicators data；The historic asset relevance data for obtaining each basic macro-indicators data, according to historic asset relevance number
Prescreening is carried out according to basic macro-indicators data；Each basic macro-indicators data and home price that calculating pre-sifted is selected are leading
The related coefficient of exponent data；The basis with home price beforehand index data correlation is filtered out according to calculated related coefficient
Macro-indicators data；The basic macro-indicators data filtered out are analyzed using Principal Component Analysis and construct property market
Foam index prediction model；The prediction of property market foam index is carried out according to property market foam index prediction model.Server
The property market foam index of prediction is returned to terminal 102 by 104.Wherein, terminal 102 can be, but not limited to be various personal meters
Calculation machine, laptop, smart phone, tablet computer and portable wearable device, server 104 can use independent service
The server cluster of device either multiple servers composition is realized.
In one embodiment, as shown in Fig. 2, providing a kind of information forecasting method, it is applied in Fig. 1 in this way
It is illustrated for server, comprising the following steps:
Step 210, home price beforehand index data are obtained, and obtain basic macro-indicators one by one from base values library
Data.
Home price beforehand index is an important indicator for measuring property market foam, and it is first that server obtains home price
Row index data, home price beforehand index data include multiple moment in preset time range, and home price refers in advance
Number numerical value.
Basic macro-indicators in base values library derive from more than 2000 a macro-performance indicators, substantially cover entire macro
See economic important indicator.These indexs have off-farm Job population, employment rate, unemployment rate, international revenue and expenditure, gross national product,
GDP, production prices index, consumer price index, disposable personal income, personal consumption expenditure, urban and rural residents' storage
The storage balance of deposits, investment target, (financial indicator includes interest rate, the exchange rate, money supply, financial institution's loans and deposits to financial indicator
Remaining sum, financial asset total amount etc.), consumption confidence index, Purchase Management Index, durable goods orders, the index numbers of industrial production, equipment
Utilization rate, retail sales index, the total retail sales of consumer goods, consumer credit, new room goes into operation and construction permit, building branch
Out, production prices index, wholesale price index, foreign trade, factory order, durable goods order, frequent account and business library
Deposit etc..
Server obtains the data of each basic macro-indicators, basic macro-indicators data packet one by one from base values library
Include the numerical value of the basic macro-indicators at multiple moment in preset time range.
Step 220, the historic asset relevance data for obtaining each basic macro-indicators data, according to historic asset relevance
Data carry out prescreening to basic macro-indicators data.
Historic asset relevance data are the historical data of each basic macro-indicators and the history of home price beforehand index
The all previous correlativity calculation result of data.Server can preset relevance threshold, by all previous of each basic macro-indicators
Correlativity calculation result is compared with relevance threshold one by one, and basis macroscopic view of the calculated result more than relevance threshold will be present
The data pre-sifted of index is elected.
Step 230, it is related to home price beforehand index data that each basic macro-indicators data that pre-sifted is selected are calculated
Coefficient.
Server calculates the phase of each basic macro-indicators data and home price beforehand index data that pre-sifted is selected one by one
Relationship number.Specifically, each basic macro-indicators data and home price beforehand index data are substituted into correlation calculations by server
Formula is calculated, and obtained calculated result is set as related coefficient.Further, server obtains the absolute of calculated result
Value, the absolute value that will acquire are set as related coefficient.Wherein, server can using Pearson correlation coefficients calculation formula, this
The formula such as Joseph Pearman correlation calculations formula carry out correlation calculations.
For example, Pearson correlation coefficients calculation formula can be used in server:
In above formula, X is basic macro-indicators data, and Y is home price beforehand index data, ρ(X,Y)For correlation system
Number.The pearson correlation property coefficient ρ of two continuous variables (X, Y)(X,Y)Equal to the covariance cov (X, Y) between them divided by it
Respectively standard deviation product (σX,σY).Always between -1.0 to 1.0, the variable close to 0 is referred to as without phase the value of coefficient
Guan Xing, being referred to as close to 1 or -1 has strong correlation.
Spearman's correlation coefficient calculation formula can also be used in server:
For server when calculating related coefficient, X is basic macro-indicators data, and Y is home price beforehand index data,
ρ is the related coefficient that server is calculated.
Step 240, it is filtered out according to calculated related coefficient macro with the basis of home price beforehand index data correlation
See achievement data.
Server obtains preset coupling index screening rule, filters out the phase relation for meeting index of correlation screening rule
Number, and the corresponding basic macro-indicators data of the related coefficient filtered out are extracted as having with home price beforehand index and are associated with
The achievement data of relationship.Coupling index screening rule can according to need the index quantity filtered out, strength of association of index etc.
Factor is specifically set.
Step 250, the basic macro-indicators data filtered out are analyzed using Principal Component Analysis and constructs assets
Market Bubble Index Prediction Model.
Server carries out principal component analysis to the multiple basic macro-indicators sequences filtered out, according to principal component analysis result
Multiple principal component expression formulas are obtained, then the principal component expression formula that obtained principal component expression formula is screened, and filtered out
Quantity is less than the quantity of associated basic macro-indicators data, and server constructs assets city according to the principal component expression formula filtered out
Field foam index prediction model.
Step 260, the prediction of property market foam index is carried out according to property market foam index prediction model.
The basic macro-indicators sequence filtered out can be substituted into Market Bubble Index Prediction Model and be counted by server
It calculates, property market foam index change curve is obtained, according to property market foam index change curve to the assets of future time instance
Market Bubble index is predicted.
In above- mentioned information prediction technique, server by obtaining multiple basic macro-indicators data from base values library,
And prescreening is carried out to basic macro-indicators data according to historic asset relevance data, and then macro according to the basis that pre-sifted is selected
It sees index series and determines associated index with the related coefficient between home price beforehand index data, thus using associated
Index constructs property market foam index prediction model, and the basic macro-indicators of the application are numerous and abundance, the money of building
It is scientific and reliable to produce Market Bubble Index Prediction Model, Accurate Prediction can be carried out to property market foam index.
In one embodiment, as shown in figure 3, being filtered out and home price beforehand index according to calculated related coefficient
The basic macro-indicators data of data correlation may include:
Step 242, default dependent thresholds are obtained, the corresponding related coefficient being calculated is extracted and is greater than default dependent thresholds
Basic macro-indicators data.
Server obtains pre-set dependent thresholds, and default dependent thresholds are for dividing each basic macro-indicators and money
Produce whether Market Bubble index has correlation, target setting when related coefficient numerical value to be greater than to default dependent thresholds be with
Correlation.Server will compare with default dependent thresholds respectively according to each basic calculated related coefficient of macro-indicators data
Compared with when server, which determines related coefficient, is greater than default dependent thresholds, corresponding basic macro-indicators data are extracted.
Step 244, the Criterion Attribute that obtains the basic macro-indicators data extracted, according to Criterion Attribute to extracting
Basic macro-indicators data are classified.
Because basis macro-indicators cover macroeconomic various aspects, there are multiple basic macro-indicators reflections are a kind of
The case where economic problems.Server carries out classification mark to each basic macro-indicators in advance, assigns different Criterion Attributes.Service
Device obtains the Criterion Attribute that the corresponding basic macro-indicators of each basic macro-indicators data are marked, and will belong to same index category
The basic macro-indicators data of property are divided into same class.
For example, the corresponding Criterion Attribute of off-farm Job population, employment rate, unemployment rate is divided into non-agricultural working attributes；State
The corresponding Criterion Attribute such as people's total output value, GDP, the index numbers of industrial production and factory order is divided into production
Data attribute；Production prices index, consumer price index, retail sales index and the total retail sales of consumer goods etc. are corresponding
Criterion Attribute is divided into sales data attribute.
Step 246, the maximum basic macro-indicators data screening of related coefficient corresponding in each classification is come out.
For example, the basic macro-indicators data that the Criterion Attribute extracted is non-agricultural working attributes include off-farm Job people
Three mouth, employment rate and unemployment rate achievement datas, the corresponding related coefficient of three achievement datas is respectively 0.6,0.8 and 0.7,
The corresponding related coefficient of employment rate index is maximum, and therefore, server screens employment rate achievement data.
In above- mentioned information prediction technique, classification mark is carried out to each basic macro-indicators in advance, and will be in every a kind of index
The maximum basic macro-indicators data screening of related coefficient comes out, it is ensured that every class index can be selected, not only reduce because
Policy, situation and time etc. influence caused by changing, and further improve the accuracy of building model.
In one embodiment, the maximum basic macro-indicators data screening of related coefficient corresponding in each classification is gone out
It can also include: to obtain the classification quantity of the basic macro-indicators data extracted, and obtain pre-set level lower limit number after coming
Value；When quantity of classifying is less than pre-set level lower numerical limit, the difference of pre-set level lower numerical limit and quantity of classifying is calculated；It will not
The basic macro-indicators data of the extraction filtered out are ranked up according to the sequence of corresponding related coefficient from large to small, before coming
The basic macro-indicators data of column also screen, and the quantity of the basic macro-indicators data stood out filtered out and poor
Value is consistent.
Server obtains preset index lower numerical limit, and obtains the classification number of the basic macro-indicators data extracted
Amount.The pre-set level lower numerical limit refers to the accuracy in order to ensure the credit risk Index Prediction Model finally constructed and sets
Minimum value, can reduce because using the influence caused by Principal Component Analysis dimensionality reduction, it is ensured that the property market finally constructed
The accuracy of foam index prediction model.For example, pre-set level lower numerical limit can be set as 10.
To classify quantity and pre-set level lower numerical limit of server is compared, when classification quantity is not less than under pre-set level
When limiting numerical value, then continues to execute and the basic macro-indicators data filtered out are analyzed using Principal Component Analysis and construct money
The step of producing Market Bubble Index Prediction Model；When quantity of classifying is less than pre-set level lower numerical limit, server calculates default
The difference Q of index lower numerical limit and classification quantity.Server is by the remaining basic macro-indicators data extracted according to corresponding
The sequence of related coefficient from large to small is ranked up, and will be come preceding Q basic macro-indicators data and also be screened.
In above- mentioned information prediction technique, pre-set level lower numerical limit is determined in advance, reduces because using principal component analysis
Influence caused by method dimensionality reduction, it is ensured that the accuracy of the property market foam index prediction model finally constructed.And when classification number
When amount is less than pre-set level lower numerical limit, the basic macro-indicators data that related coefficient numerical value is stood out are extracted, not only more
Influence caused by lazy weight is mended, and each basic macro-indicators data are closely closed with property market foam index data
Connection, it is ensured that property market foam index prediction model it is accurate.
In one embodiment, the maximum basic macro-indicators data screening of related coefficient corresponding in each classification is gone out
It can also include: that asset association label is carried out to the corresponding related coefficient of the basic macro-indicators data filtered out after coming；It will
Calculated each related coefficient is added to the historic asset relevance data of corresponding basic macro-indicators data.
The corresponding related coefficient of basic macro-indicators filtered out is marked server, is labeled as property market foam
Each related coefficient is associated storage with corresponding basic macro-indicators data, will calculated by coupling index coefficient, server
Assets correlation data of the related coefficient as basic macro-indicators data.Carrying out, property market foam index next time is pre-
When survey, server can directly search the assets correlation data of each basic macro-indicators, and judge the phase relation that history calculates
Whether number has carried out assets mark of correlation, and the basic macro-indicators pre-sifted for having carried out assets mark of correlation is elected, so as to
It is enough substantially reduced the screening range of basic macro-indicators, reduces calculation amount, and improve exponential forecasting efficiency.
In one embodiment, analyze simultaneously structure to the basic macro-indicators data filtered out using Principal Component Analysis
Building property market foam index prediction model may include: to be standardized to the basic macro-indicators data filtered out
To data matrix；The covariance matrix of data matrix is calculated, and calculates the characteristic root and feature vector of covariance matrix；According to spy
Sign root and feature vector determine principal component expression formula；Property market foam index prediction model is constructed according to principal component expression formula.
Server acquires p dimension random vector x=(x to the n basic macro-indicators data filtered out respectively1,x2,...,
xp)T, wherein each basis macro-indicators data xi=(xi1,xi2,...,xip)T, i=1,2 ..., n, n > p constructs square to it
Battle array, the difference between dimension difference and the order of magnitude carrying out following standardized transformation to matrix array elements to eliminate different achievement datas
And the data matrix Z after must standardizingij:
Wherein, ZijIndicate the data matrix of j-th of numerical value in i-th of basic macro-indicators sequence,N is the total quantity of basic macro-indicators sequence, and p is each basic macro-indicators sequence
Arrange numerical value number in corresponding ordered series of numbers.
Covariance matrix R is acquired according to data matrix Z, the calculation formula of covariance matrix R is as follows:
Then solve covariance matrix R and obtain p characteristic root, and obtain the principal component variance contribution ratio of p characteristic root with
And corresponding feature vectorThe target variable after standardization is converted into principal component according to characteristic root and feature vector, it is as follows
It is shown:
U1Referred to as first principal component expression formula, U2Referred to as Second principal component, expression formula ..., UpReferred to as pth principal component is expressed
Formula.
Server can construct rule according to preset model and choose principal component expression formula and construct property market foam index
Prediction model.For example, it is credit risk Index Prediction Model that server, which can choose first principal component expression formula, server can also
To calculate the principal component variance contribution ratio of each principal component expression formula, and preset minimum variance contribution ratio threshold value is obtained,
The principal component expression formula that principal component variance contribution ratio is more than minimum variance contribution ratio threshold value is extracted, and to the principal component extracted
Expression formula is weighted summation and obtains credit risk Index Prediction Model, wherein weight can be set as each principal component expression
The principal component variance contribution ratio of formula.The independent variable for the property market foam index prediction model that server obtains is after standardizing
The basic macro-indicators data filtered out, dependent variable are considered as home price beforehand index i.e. property market foam index, from
Obtained from property market foam index be multiple basic macro-indicators data filtered out the sum of weighting.
In one embodiment, each basic macro-indicators data and home price beforehand index data that pre-sifted is selected are calculated
Related coefficient may include: obtain predetermined sequence initial time；Extraction and predetermined sequence from each basic macro-indicators data
The corresponding basic macro-indicators sequence of initial time, and when extracting initial with predetermined sequence from home price beforehand index data
Carve corresponding home price sequence；Calculate the related coefficient of each basic macro-indicators sequence and home price sequence.
Server obtains preset sequence initial time, and the quantity of predetermined sequence initial time can be multiple.Server
Each predetermined sequence initial time is obtained one by one, and by the corresponding numerical value of predetermined sequence initial time each in basic macro-indicators and in advance
If all moment corresponding numerical value extracts as basic macro-indicators sequence after sequence initial time, to obtain multiple weeks times
Phase includes multiple basic macro-indicators sequences.
For example, the data for including in each basis macro-indicators are by the data in March, 2018, when predetermined sequence is initial
Carving is respectively in March, 2008, April, May and June, then extracts 4 basis macroscopic views respectively from each basic macro-indicators data
Index series, the respectively index series in March, 2008 in March, 2018, the index series in April, 2008 in March, 2018,
The index series and the index series in June, 2008 in March, 2018 in May, 2008 in March, 2018.
Server searches the corresponding numerical value of predetermined sequence initial time and default sequence from home price beforehand index data
All moment corresponding numerical value, is extracted as home price sequence for the numerical value found, and calculate more after column initial time
The related coefficient of a predetermined sequence initial time corresponding each basic macro-indicators sequence and home price sequence, when obtaining multiple
Between the period related coefficient data, to realize the prediction of the property market foam index of multiple time cycles.
In one embodiment, the basic macro-indicators sequence filtered out is inputted each group property market bubble by server respectively
Foam Index Prediction Model obtains a plurality of property market foam index prediction curve；It is pre- according to each property market foam index respectively
Survey the property market foam index predicted value that curve calculates the corresponding future time instance of each predetermined sequence initial time.
Server obtains the corresponding basic macro-indicators sequence filtered out of each time cycle one by one, and is input to corresponding
Property market foam index prediction model in, to obtain the scatterplot value of the property market foam index at multiple moment, according to
Multiple scatterplot values draw property market foam index prediction curve.
Server is respectively fitted obtained a plurality of property market foam index prediction curve, and fit procedure can adopt
With linear fit or non-linear fitting method, predicted according to the property market foam index that fitting result calculates future time instance
Value.Specifically, future time instance can be corresponding with predetermined sequence initial time, and the initial time of sequence is more late, when corresponding following
It carves also more late.
For example, the basic macro-indicators sequence filtered out in March, 2008 in March, 2018 is substituted into money by server
It produces in Market Bubble Index Prediction Model, predetermined sequence initial time in March, 2008, corresponding future time instance for next month was
It can predict the property market foam index in April, 2018 first phase in April, 2018；By in April, 2008 in March, 2018
Home price sequence substitutes into property market foam index prediction model, and predetermined sequence initial time is corresponding not in April, 2008
Carrying out the moment is lower two months i.e. in April, 2018 and in May, 2018, can predict the property market foam in May, 2018 second phase
Index, and so on, so as to predict the property market foam index of following more phases.
As shown in figure 4, server constructs the 1st time cycle to (2018 the 2nd the 4th time cycle using this method respectively
Season to the first quarter in 2019) property market foam index prediction model.Predetermined sequence initial time is the 3rd season in 2011
Degree, server extract home price sequence corresponding with 2011 the 2nd phase (third season) from home price beforehand index data
It arranges (OBJ curve in figure), server extracts basis corresponding with predetermined sequence initial time from each basic macro-indicators data
Macro-indicators sequence.
When constructing the property market foam index prediction model of the 1st time cycle, server is from each base values data
Middle to extract from the starting third quarter in 2011 to the corresponding basis macro-indicators sequence first quarter in 2018, property market is steeped at this time
Foam Index Prediction Model is OBJ1 curve in figure.When constructing the property market foam index prediction model of the 2nd time cycle, clothes
Business device extracts to originate from the fourth quarter in 2011 to basis in the corresponding first quarter in 2018 from each basic macro-indicators data to be referred to
Sequence is marked, property market foam index prediction model is OBJ2 curve in figure at this time.When the property market for constructing for the 3rd time cycle
When foam index prediction model, server is extracted from each base values data to be originated from the first quarter in 2012 to corresponding
The first quarter in 2018 base values sequence, at this time property market foam index prediction model be figure in OBJ3 curve.When building the
When the real assets Market Bubble Index Prediction Model of 4 time cycles, server is extracted from each base values data from 2012
To the corresponding base values sequence first quarter in 2018, borrowing risk index prediction model at this time is in figure for starting in the second quarter in year
OBJ4 curve.
When using the property market foam index in this method prediction different time period, it is required to first construct property market
Foam index prediction model, to ensure the uniqueness of the building index of each phase.It can be reduced in this way because policy, situation are with timely
Between influence caused by equal variation, further promote the accuracy of the property market foam index of each phase.
It should be understood that although each step in the flow chart of Fig. 2-3 is successively shown according to the instruction of arrow,
These steps are not that the inevitable sequence according to arrow instruction successively executes.Unless expressly stating otherwise herein, these steps
Execution there is no stringent sequences to limit, these steps can execute in other order.Moreover, at least one in Fig. 2-3
Part steps may include that perhaps these sub-steps of multiple stages or stage are not necessarily in synchronization to multiple sub-steps
Completion is executed, but can be executed at different times, the execution sequence in these sub-steps or stage is also not necessarily successively
It carries out, but can be at least part of the sub-step or stage of other steps or other steps in turn or alternately
It executes.
In one embodiment, as shown in figure 5, providing a kind of information prediction device, comprising: data acquisition module 510,
Pre-screening module 520, coefficients calculation block 530, index screening module 540, model construction module 550 and exponential forecasting module
560, in which:
Data acquisition module 510 obtains one by one for obtaining home price beforehand index data, and from base values library
Basic macro-indicators data.
Pre-screening module 520, for obtaining the historic asset relevance data of each basic macro-indicators data, according to history
Assets correlation data carry out prescreening to basic macro-indicators data.
Coefficients calculation block 530 refers to for calculating each basic macro-indicators data that pre-sifted is selected with home price in advance
The related coefficient of number data.
Index screening module 540, for being filtered out and home price beforehand index data according to calculated related coefficient
Associated basis macro-indicators data.
Model construction module 550, for being divided using Principal Component Analysis the basic macro-indicators data filtered out
It analyses and constructs property market foam index prediction model.
Exponential forecasting module 560, for carrying out property market foam index according to property market foam index prediction model
Prediction.
In one embodiment, index screening module 540 may include:
Threshold value acquisition submodule extracts the corresponding related coefficient being calculated and is greater than for obtaining default dependent thresholds
The basic macro-indicators data of default dependent thresholds.
Data classification submodule, for obtaining the Criterion Attribute of the basic macro-indicators data extracted, according to index category
Property classifies to the basic macro-indicators data extracted.
Submodule is screened, for going out the maximum basic macro-indicators data screening of related coefficient corresponding in each classification
Come.
In one embodiment, information prediction device can also include:
Connective marker submodule, for carrying out assets pass to the corresponding related coefficient of the basic macro-indicators data filtered out
Connection label.
Data sub-module stored, for calculated each related coefficient to be added to going through for corresponding basic macro-indicators data
History assets correlation data.
In one embodiment, index screening module 540 can also include:
Numerical value acquisition submodule for obtaining the classification quantity of the basic macro-indicators data extracted, and obtains default
Index lower numerical limit.
Difference calculating module, for calculating pre-set level lower limit number when quantity of classifying is less than pre-set level lower numerical limit
The difference of value and classification quantity.
Final election submodule, for by the basic macro-indicators data for the extraction not filtered out according to corresponding related coefficient by big
It is ranked up, the basic macro-indicators data stood out also is screened, and what is filtered out stands out to small sequence
Basic macro-indicators data quantity it is consistent with difference.
In one embodiment, model construction module 550 may include:
Normalizer module, for being standardized to obtain data square to the basic macro-indicators data filtered out
Battle array.
Covariance computational submodule for calculating the covariance matrix of data matrix, and calculates the feature of covariance matrix
Root and feature vector.
Expression formula determines submodule, for determining principal component expression formula according to characteristic root and feature vector.
Submodule is constructed, for constructing property market foam index prediction model according to principal component expression formula.
In one embodiment, coefficients calculation block 530 may include:
Moment acquisition submodule, for obtaining predetermined sequence initial time.
Sequential extraction procedures submodule, it is corresponding with predetermined sequence initial time for being extracted from each basic macro-indicators data
Basic macro-indicators sequence, and house corresponding with predetermined sequence initial time valence is extracted from home price beforehand index data
Lattice sequence.
Relevant calculation submodule, for calculating the related coefficient of each basic macro-indicators sequence and home price sequence.
Specific about information prediction device limits the restriction that may refer to above for information forecasting method, herein not
It repeats again.Modules in above- mentioned information prediction meanss can be realized fully or partially through software, hardware and combinations thereof.On
Stating each module can be embedded in the form of hardware or independently of in the processor in computer equipment, can also store in a software form
In memory in computer equipment, the corresponding operation of the above modules is executed in order to which processor calls.
In one embodiment, a kind of computer equipment is provided, which can be server, internal junction
Composition can be as shown in Figure 6.The computer equipment include by system bus connect processor, memory, network interface and
Database.Wherein, the processor of the computer equipment is for providing calculating and control ability.The memory packet of the computer equipment
Include non-volatile memory medium, built-in storage.The non-volatile memory medium is stored with operating system, computer program and data
Library.The built-in storage provides environment for the operation of operating system and computer program in non-volatile memory medium.The calculating
The database of machine equipment is for storing information prediction related data.The network interface of the computer equipment is used for and external terminal
It is communicated by network connection.To realize a kind of information forecasting method when the computer program is executed by processor.
It will be understood by those skilled in the art that structure shown in Fig. 6, only part relevant to application scheme is tied
The block diagram of structure does not constitute the restriction for the computer equipment being applied thereon to application scheme, specific computer equipment
It may include perhaps combining certain components or with different component layouts than more or fewer components as shown in the figure.
In one embodiment, a kind of computer equipment, including memory and processor are provided, which is stored with
Computer program, which performs the steps of when executing computer program obtains home price beforehand index data, and from
Basic macro-indicators data are obtained in base values library one by one；Obtain the historic asset relevance number of each basic macro-indicators data
According to according to historic asset relevance data to basic macro-indicators data progress prescreening；It is macro to calculate each basis that pre-sifted is selected
See the related coefficient of achievement data and home price beforehand index data；It is filtered out and house valence according to calculated related coefficient
The basic macro-indicators data of lattice beforehand index data correlation；Using Principal Component Analysis to the basic macro-indicators number filtered out
According to being analyzed and construct property market foam index prediction model；Assets are carried out according to property market foam index prediction model
Market Bubble exponential forecasting.
In one embodiment, processor execute computer program when realize according to calculated related coefficient filter out with
It is also used to when the step of the basic macro-indicators data of home price beforehand index data correlation: obtaining default dependent thresholds, mention
Take out the basic macro-indicators data that the corresponding related coefficient being calculated is greater than default dependent thresholds；Obtain the basis extracted
The Criterion Attribute of macro-indicators data classifies to the basic macro-indicators data extracted according to Criterion Attribute；It will be each
The maximum basic macro-indicators data screening of corresponding related coefficient comes out in classification.
In one embodiment, it is also performed the steps of when processor executes computer program macro to the basis filtered out
It sees the corresponding related coefficient of achievement data and carries out asset association label；It is macro that calculated each related coefficient is added to corresponding basis
See the historic asset relevance data of achievement data.
In one embodiment, the basis for obtaining and extracting also is performed the steps of when processor executes computer program
The classification quantity of macro-indicators data, and obtain pre-set level lower numerical limit；When classification quantity is less than pre-set level lower numerical limit
When, calculate the difference of pre-set level lower numerical limit and quantity of classifying；By the basic macro-indicators data root for the extraction not filtered out
It is ranked up according to the sequence of corresponding related coefficient from large to small, the basic macro-indicators data stood out also is screened,
And the quantity of the basic macro-indicators data stood out filtered out is consistent with difference.
In one embodiment, it realizes using Principal Component Analysis when processor executes computer program to the base filtered out
Plinth macro-indicators data are analyzed and are also used to when constructing the step of property market foam index prediction model: to what is filtered out
Basic macro-indicators data are standardized to obtain data matrix；The covariance matrix of data matrix is calculated, and calculates association
The characteristic root and feature vector of variance matrix；Principal component expression formula is determined according to characteristic root and feature vector；According to principal component table
Property market foam index prediction model is constructed up to formula.
In one embodiment, it is realized when processor executes computer program and calculates each basic macro-indicators that pre-sifted is selected
It is also used to when data and the step of the related coefficient of home price beforehand index data: obtaining predetermined sequence initial time；From each
Basic macro-indicators sequence corresponding with predetermined sequence initial time is extracted in basic macro-indicators data, and first from home price
Home price sequence corresponding with predetermined sequence initial time is extracted in row index data；Calculate each basic macro-indicators sequence with
The related coefficient of home price sequence.
In one embodiment, a kind of computer readable storage medium is provided, computer program is stored thereon with, is calculated
Machine program performed the steps of when being executed by processor obtain home price beforehand index data, and from base values library by
The basic macro-indicators data of a acquisition；The historic asset relevance data for obtaining each basic macro-indicators data, provide according to history
It produces relevance data and prescreening is carried out to basic macro-indicators data；Calculate each basic macro-indicators data and room that pre-sifted is selected
The related coefficient of room price beforehand index data；It is filtered out and home price beforehand index data according to calculated related coefficient
Associated basis macro-indicators data；Analyze simultaneously structure to the basic macro-indicators data filtered out using Principal Component Analysis
Build property market foam index prediction model；It is pre- that property market foam index is carried out according to property market foam index prediction model
It surveys.
In one embodiment, it realizes when computer program is executed by processor and is filtered out according to calculated related coefficient
It is also used to when with the steps of the basic macro-indicators data of home price beforehand index data correlation: obtaining default dependent thresholds,
Extract the basic macro-indicators data that the corresponding related coefficient being calculated is greater than default dependent thresholds；Obtain the base extracted
The Criterion Attribute of plinth macro-indicators data classifies to the basic macro-indicators data extracted according to Criterion Attribute；It will be every
The maximum basic macro-indicators data screening of corresponding related coefficient comes out in one classification.
In one embodiment, it also performs the steps of when computer program is executed by processor to the basis filtered out
The corresponding related coefficient of macro-indicators data carries out asset association label；Calculated each related coefficient is added to corresponding basis
The historic asset relevance data of macro-indicators data.
In one embodiment, the base for obtaining and extracting also is performed the steps of when computer program is executed by processor
The classification quantity of plinth macro-indicators data, and obtain pre-set level lower numerical limit；When classification quantity is less than pre-set level lower limit number
When value, the difference of pre-set level lower numerical limit and quantity of classifying is calculated；By the basic macro-indicators data for the extraction not filtered out
It is ranked up according to the sequence of corresponding related coefficient from large to small, the basic macro-indicators data stood out also is filtered out
Come, and the quantity of the basic macro-indicators data stood out filtered out is consistent with difference.
In one embodiment, computer program is realized when being executed by processor using Principal Component Analysis to filtering out
Basic macro-indicators data are analyzed and are also used to when constructing the step of property market foam index prediction model: to filtering out
Basic macro-indicators data be standardized to obtain data matrix；The covariance matrix of data matrix is calculated, and is calculated
The characteristic root and feature vector of covariance matrix；Principal component expression formula is determined according to characteristic root and feature vector；According to principal component
Expression formula constructs property market foam index prediction model.
In one embodiment, realization is also performed the steps of when computer program is executed by processor calculates prescreening
It is also used to when each basic macro-indicators data and the step of the related coefficient of home price beforehand index data out: obtaining and preset
Sequence initial time；Basic macro-indicators sequence corresponding with predetermined sequence initial time is extracted from each basic macro-indicators data
Column, and home price sequence corresponding with predetermined sequence initial time is extracted from home price beforehand index data；It calculates each
The related coefficient of basic macro-indicators sequence and home price sequence.
Those of ordinary skill in the art will appreciate that realizing all or part of the process in above-described embodiment method, being can be with
Relevant hardware is instructed to complete by computer program, the computer program can be stored in a non-volatile computer
In read/write memory medium, the computer program is when being executed, it may include such as the process of the embodiment of above-mentioned each method.Wherein,
To any reference of memory, storage, database or other media used in each embodiment provided herein,
Including non-volatile and/or volatile memory.Nonvolatile memory may include read-only memory (ROM), programming ROM
(PROM), electrically programmable ROM (EPROM), electrically erasable ROM (EEPROM) or flash memory.Volatile memory may include
Random access memory (RAM) or external cache.By way of illustration and not limitation, RAM is available in many forms,
Such as static state RAM (SRAM), dynamic ram (DRAM), synchronous dram (SDRAM), double data rate sdram (DDRSDRAM), enhancing
Type SDRAM (ESDRAM), synchronization link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM
(RDRAM), direct memory bus dynamic ram (DRDRAM) and memory bus dynamic ram (RDRAM) etc..
Each technical characteristic of above embodiments can be combined arbitrarily, for simplicity of description, not to above-described embodiment
In each technical characteristic it is all possible combination be all described, as long as however, the combination of these technical characteristics be not present lance
Shield all should be considered as described in this specification.
The several embodiments of the application above described embodiment only expresses, the description thereof is more specific and detailed, but simultaneously
It cannot therefore be construed as limiting the scope of the patent.It should be pointed out that coming for those of ordinary skill in the art
It says, without departing from the concept of this application, various modifications and improvements can be made, these belong to the protection of the application
Range.Therefore, the scope of protection shall be subject to the appended claims for the application patent.
Claims (10)
1. a kind of information forecasting method, which comprises
Home price beforehand index data are obtained, and obtain basic macro-indicators data one by one from base values library；
The historic asset relevance data for obtaining each basic macro-indicators data, according to the historic asset relevance data
Prescreening is carried out to the basic macro-indicators data；
Calculate the related coefficient of each basic the macro-indicators data and the home price beforehand index data that pre-sifted is selected；
It is filtered out according to the calculated related coefficient and is referred to the basic macroscopic view of the home price beforehand index data correlation
Mark data；
The basic macro-indicators data filtered out are analyzed using Principal Component Analysis and construct property market foam
Index Prediction Model；
The prediction of property market foam index is carried out according to the property market foam index prediction model.
2. the method according to claim 1, wherein it is described according to the calculated related coefficient filter out with
The basic macro-indicators data of the home price beforehand index data correlation, comprising:
Default dependent thresholds are obtained, the base that the corresponding related coefficient being calculated is greater than the default dependent thresholds is extracted
Plinth macro-indicators data；
The Criterion Attribute for obtaining the basic macro-indicators data extracted, according to the Criterion Attribute to described in extracting
Basic macro-indicators data are classified；
The maximum basic macro-indicators data screening of related coefficient corresponding in each classification is come out.
3. according to the method described in claim 2, it is characterized in that, it is described by the related coefficient corresponding in each classification most
After the big basic macro-indicators data screening comes out, further includes:
Asset association label is carried out to the corresponding related coefficient of the basis macro-indicators data filtered out；
Calculated each related coefficient is added to the historic asset relevance data of corresponding basic macro-indicators data.
4. according to the method described in claim 2, it is characterized in that, it is described by the related coefficient corresponding in each classification most
After the big basic macro-indicators data screening comes out, further includes:
The classification quantity of the basic macro-indicators data extracted is obtained, and obtains pre-set level lower numerical limit；
When the classification quantity is less than the pre-set level lower numerical limit, the pre-set level lower numerical limit and described point are calculated
The difference of class quantity；
The basic macro-indicators data for the extraction not filtered out are carried out according to the sequence of corresponding related coefficient from large to small
Sequence, the basis stood out that the basic macro-indicators data stood out also are screened, and filtered out
The quantity of macro-indicators data is consistent with the difference.
5. the method according to claim 1, wherein it is described using Principal Component Analysis to the base filtered out
Plinth macro-indicators data are analyzed and construct property market foam index prediction model, comprising:
The basic macro-indicators data filtered out are standardized to obtain data matrix；
The covariance matrix of the data matrix is calculated, and calculates the characteristic root and feature vector of the covariance matrix；
Principal component expression formula is determined according to the characteristic root and described eigenvector；
Property market foam index prediction model is constructed according to the principal component expression formula.
6. the method according to claim 1, wherein each basic macro-indicators for calculating pre-sifted and selecting
The related coefficient of data and the home price beforehand index data, comprising:
Obtain predetermined sequence initial time；
Basic macro-indicators sequence corresponding with the predetermined sequence initial time is extracted from each basic macro-indicators data
Column, and home price sequence corresponding with the predetermined sequence initial time is extracted from the home price beforehand index data
Column；
Calculate the related coefficient of each basic the macro-indicators sequence and the home price sequence.
7. a kind of information prediction device, which is characterized in that described device includes:
Data acquisition module, for obtaining home price beforehand index data, and from base values library, acquisition basis is macro one by one
See achievement data；
Pre-screening module is gone through for obtaining the historic asset relevance data of each basic macro-indicators data according to described
History assets correlation data carry out prescreening to the basic macro-indicators data；
Coefficients calculation block refers to for calculating each basic macro-indicators data that pre-sifted is selected with the home price in advance
The related coefficient of number data；
Index screening module, for being filtered out and the home price beforehand index data according to the calculated related coefficient
Associated basis macro-indicators data；
Model construction module, for being analyzed simultaneously using Principal Component Analysis the basic macro-indicators data filtered out
Construct property market foam index prediction model；
Exponential forecasting module, it is pre- for carrying out property market foam index according to the property market foam index prediction model
It surveys.
8. device according to claim 7, which is characterized in that the index screening module includes:
Threshold value acquisition submodule extracts the corresponding related coefficient being calculated and is greater than for obtaining default dependent thresholds
The basic macro-indicators data of the default dependent thresholds；
Data classification submodule, for obtaining the Criterion Attribute of the basic macro-indicators data extracted, according to the finger
Mark attribute classifies to the basic macro-indicators data extracted；
Submodule is screened, for sieving the maximum basic macro-indicators data of related coefficient corresponding in each classification
It elects.
9. a kind of computer equipment, including memory and processor, the memory are stored with computer program, feature exists
In the step of processor realizes any one of claims 1 to 6 the method when executing the computer program.
10. a kind of computer readable storage medium, is stored thereon with computer program, which is characterized in that the computer program
The step of method described in any one of claims 1 to 6 is realized when being executed by processor.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110664121A (en) * | 2019-10-05 | 2020-01-10 | 扬州市君瑞企业管理有限公司 | Prediction device for static investment of power transformation project |
| CN112802603A (en) * | 2021-02-04 | 2021-05-14 | 北京深演智能科技股份有限公司 | Method and device for predicting influenza degree |
| CN113095604A (en) * | 2021-06-09 | 2021-07-09 | 平安科技（深圳）有限公司 | Fusion method, device and equipment of product data and storage medium |
| CN114154697A (en) * | 2021-11-19 | 2022-03-08 | 中国建设银行股份有限公司 | House maintenance resource prediction method and device, computer equipment and storage medium |
-
2019
- 2019-03-21 CN CN201910218862.4A patent/CN110110886A/en active Pending
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110664121A (en) * | 2019-10-05 | 2020-01-10 | 扬州市君瑞企业管理有限公司 | Prediction device for static investment of power transformation project |
| CN112802603A (en) * | 2021-02-04 | 2021-05-14 | 北京深演智能科技股份有限公司 | Method and device for predicting influenza degree |
| CN113095604A (en) * | 2021-06-09 | 2021-07-09 | 平安科技（深圳）有限公司 | Fusion method, device and equipment of product data and storage medium |
| CN113095604B (en) * | 2021-06-09 | 2021-09-10 | 平安科技（深圳）有限公司 | Fusion method, device and equipment of product data and storage medium |
| CN114154697A (en) * | 2021-11-19 | 2022-03-08 | 中国建设银行股份有限公司 | House maintenance resource prediction method and device, computer equipment and storage medium |
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