CN114707729B - Population quantity prediction method and device for old people, computer equipment and storage medium - Google Patents
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Abstract
The embodiment of the invention discloses a population quantity prediction method and device for old people, computer equipment and a storage medium. The method comprises the following steps: acquiring historical population data in a certain level of area, and complementing the missing index to obtain initial data; inputting the initial data into an ARIMA model to perform fitting and prediction on age, sex population mobility, migration rate and death rate so as to obtain a predicted value; calculating population retention according to the predicted value; and inputting the initial data and the population retention into an age shifting model to shift the population of a certain age to the next age of the next year under the corresponding probability condition, and predicting the population of the old people to obtain a prediction result. By implementing the method provided by the embodiment of the invention, the availability of data and the reliability of the model can be improved, and the accuracy of the prediction of the aged population is improved.
Description
Technical Field
The present invention relates to population number prediction methods, and more particularly to population number prediction methods, devices, computer apparatuses, and storage media for elderly people.
Background
Population quantity is an important social resource, and prediction of population quantity can provide data support and evaluation basis for making social planning and developing related policies, and population aging is already a trend of development in China, so that understanding, analyzing and predicting population data of the elderly is of full practical significance for promoting support and service establishment of aged people and is an important data base.
The existing population quantity prediction scheme of the old is concentrated on national data, and is not powerful in supporting data of local areas; the data sources are single, the population number is divided by the age groups of 5 years, the obtained prediction results take 5 years as prediction intervals, the time span is large, and the accuracy and the precision of the prediction results are not ideal; most of the population prediction data about the population of the aged lacks age-divided and sex-divided population prediction data of the aged; most of the population migration factors are not considered, and population fluidity problems are ignored.
Therefore, it is necessary to design a new method to improve the availability of data and the reliability of models and the accuracy of the prediction of the aged population.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a population quantity prediction method, device, computer equipment and storage medium for the aged.
In order to achieve the above purpose, the present invention adopts the following technical scheme: a method of predicting population of elderly people comprising:
acquiring historical population data in a certain level of area, and complementing the missing index to obtain initial data;
inputting the initial data into an ARIMA model to perform fitting and prediction on age, sex population mobility, migration rate and death rate so as to obtain a predicted value;
calculating population retention according to the predicted value;
and inputting the initial data and the population retention into an age shifting model to shift the population of a certain age to the next age of the next year under the corresponding probability condition, and predicting the population of the old people to obtain a prediction result.
The further technical scheme is as follows: the step of obtaining historical population data in a certain level of area and complementing the missing index to obtain initial data comprises the following steps:
acquiring historical population data in a certain level of area;
and complementing the indexes missing in the historical population data by adopting a Lagrange interpolation method to obtain initial data.
The further technical scheme is as follows: the initial data is input into an ARIMA model to be fitted and predicted for age, sex population mobility, migration rate and death rate to obtain a predicted value, and the method comprises the following steps:
Generating a population time series set according to the initial data;
performing stationarity test on the population time series set to obtain a verification result;
establishing an ARIMA model, and fitting the verification result by using the ARIMA model;
performing white noise test on the residual sequence of the ARIMA model to obtain a test result;
judging whether the test result receives a white noise original hypothesis or not;
if the test result receives the white noise source hypothesis, selecting an ARIMA model with relatively optimal overall prediction effect of population mobility, population mobility and population mortality at each age and each gender by voting, and determining a prediction result of the ARIMA model with relatively optimal prediction effect to obtain a prediction value.
The further technical scheme is as follows: the population time series set includes a population mobility time series set, and a population mortality time series set.
The further technical scheme is as follows: the calculating population retention according to the predicted value comprises the following steps:
determining a retention prediction function according to the form of the exponential function;
and inputting the predicted value into the retention prediction function to obtain the population retention.
The further technical scheme is as follows: inputting the initial data and population retention into an age shifting model to shift population of a certain age to a next age of a next year under a corresponding probability condition, and predicting population of the elderly to obtain a prediction result, wherein the method comprises the following steps of:
determining population numbers corresponding to ages and sexes in a certain level of area according to the initial data;
and inputting the population quantity corresponding to the ages and sexes in a certain level of region and the population retention rate into an age shift model, calculating the population quantity of the next age of the next year corresponding to the sexes, and analogizing the population quantity of the aged with the designated years after a plurality of years to obtain a prediction result.
The further technical scheme is as follows: the number of aged people with specified years after several years is Wherein t+n represents n years from the prediction starting point year t to the back, and x represents a designated time;representing the population count corresponding to the last year and last age of the gender.
The invention also provides a population quantity prediction device for the aged, which comprises the following steps:
the data processing unit is used for acquiring historical population data in a certain level of area and complementing the missing indexes to obtain initial data;
The fitting prediction unit is used for inputting the initial data into an ARIMA model to perform fitting and prediction on age, sex population mobility, migration rate and death rate so as to obtain a predicted value;
the retention rate calculation unit is used for calculating population retention rate according to the predicted value;
and the population prediction unit is used for inputting the initial data and the population retention into the age shift model so as to shift the population of a certain age in a certain year to the next age in the next year under the corresponding probability condition, and predicting the population of the old people so as to obtain a prediction result.
The invention also provides a computer device which comprises a memory and a processor, wherein the memory stores a computer program, and the processor realizes the method when executing the computer program.
The present invention also provides a storage medium storing a computer program which, when executed by a processor, implements the above method.
Compared with the prior art, the invention has the beneficial effects that: according to the invention, historical population data is obtained, and the Lagrange interpolation method is adopted to complement the missing data, so that the availability of the data and the reliability of the model are improved; the age interval is shortened, so that the prediction precision is greatly improved, and the final prediction result is taken as a unit every year; the overall relative optimal ARIMA model is selected according to the voting principle, so that the complexity is reduced, the model is prevented from being too complicated and complicated, and the overall energy consumption is reduced; mobility factors are emphasized, mobility, migration rate and death rate are predicted, population retention rate is calculated, and the accuracy of old people prediction is improved.
The invention is further described below with reference to the drawings and specific embodiments.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic diagram of an application scenario of a method for predicting population numbers of elderly people according to an embodiment of the present invention;
fig. 2 is a flow chart of a method for predicting population numbers of elderly people according to an embodiment of the present invention;
fig. 3 is a schematic sub-flowchart of a method for predicting population numbers of elderly people according to an embodiment of the present invention;
fig. 4 is a schematic sub-flowchart of a method for predicting population numbers of elderly people according to an embodiment of the present invention;
fig. 5 is a schematic sub-flowchart of a method for predicting population numbers of elderly people according to an embodiment of the present invention;
fig. 6 is a schematic sub-flowchart of a method for predicting population numbers of elderly people according to an embodiment of the present invention;
fig. 7 is a schematic sub-flowchart of a method for predicting population numbers of elderly people according to an embodiment of the present invention;
FIG. 8 is a schematic block diagram of a data processing unit of the device for predicting population of elderly people according to an embodiment of the present invention;
FIG. 9 is a schematic block diagram of a fit prediction unit of an elderly population quantity prediction apparatus provided by an embodiment of the present invention;
fig. 10 is a schematic block diagram of a retention calculating unit of the device for predicting the population quantity of the elderly according to the embodiment of the present invention;
FIG. 11 is a schematic block diagram of a population prediction unit of an elderly population quantity prediction apparatus provided by an embodiment of the present invention;
fig. 12 is a schematic block diagram of a computer device according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be understood that the terms "comprises" and "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in the present specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
Referring to fig. 1 and fig. 2, fig. 1 is a schematic diagram of an application scenario of a method for predicting population numbers of elderly people according to an embodiment of the present invention. Fig. 2 is a schematic flow chart of a method for predicting population numbers of elderly people according to an embodiment of the present invention. The population quantity prediction method for the elderly is applied to the server. The server performs data interaction with the terminal, historical population data in a certain level area is obtained through the terminal, missing index data are complemented, an ARIMA model is introduced to fit population mobility, migration rate and death rate, a relatively optimal model of the overall prediction effect is selected through a voting mode, a prediction value is obtained, a retention rate function is built, an age shifting model is introduced, population quantity of the old is predicted through the population retention rate prediction value, the availability of the data and the reliability of the model are improved, and the accuracy of old population prediction is improved.
Fig. 2 is a flow chart of a method for predicting population numbers of elderly people according to an embodiment of the present invention. As shown in fig. 2, the method includes the following steps S110 to S140.
S110, acquiring historical population data in a certain level of area, and complementing the missing index to obtain initial data.
In this embodiment, the initial data is data obtained by acquiring historical population data of administrative units such as provinces, district cities, regions and counties in a certain level of area, and supplementing the missing index.
In one embodiment, referring to fig. 3, the step S110 may include steps S111 to S112.
S111, acquiring historical population data in a certain level of area.
In this embodiment, the historical population data includes age, gender, population number, population mobility, migration rate, and mortality.
S112, complementing the indexes missing in the historical population data by adopting a Lagrange interpolation method to obtain initial data.
In the present embodiment of the present invention, in the present embodiment,and supplementing the missing index data by adopting a Lagrange interpolation method, specifically, constructing a coordinate system taking the year t as a horizontal axis and the index data D as a vertical axis, and converting the data into a point set in a coordinate system plane. Assume year t 1 ,t 2 ,…,t n Corresponding index data D 1 ,D 2 ,…,D n Wherein year t m Corresponding data D m Missing, constructing a Lagrange interpolation function by using the missing: with L (t) m ) As D m And (3) performing deletion complement on the original data, namely the historical population data in a certain level of area.
Focusing on the number of the aged people in a certain level area, for the situation that partial data are missing in reality, the invention complements the missing data by introducing the Lagrange interpolation method, and improves the availability of the data and the reliability of the model.
And S120, inputting the initial data into an ARIMA model to perform fitting and prediction on age, sex population mobility, migration rate and death rate so as to obtain a predicted value.
In the present embodiment, the predicted value refers to a predicted value of population mobility, population mortality at each age and each sex.
In one embodiment, referring to fig. 4, the step S120 may include steps S121 to S126.
S121, generating a population time sequence set according to the initial data.
In this embodiment, the population time series set includes population mobility time series setsPopulation mobility time series set +.>Population mortality time series set->Where a represents age, s represents gender, and t represents year time.
Focusing on the number of the aged people in a certain level area, under the premise of considering death factors, the migration factors are emphasized, and meanwhile mobility, mobility and death rate are concerned, so that the accuracy of the aged population prediction is further improved.
Each time series set is a set of time-dependent data generated from each corresponding data in the initial data.
S122, carrying out stability test on the population time series set to obtain a verification result.
In this embodiment, the verification result refers to a stationarity verification result of the population time series set.
Specifically, the population time series set is subjected to stationarity test, and the result of receiving the non-stationary original hypothesis is obtained through Phillips-Perron unit root test.
The original assumption is that a unit root exists in a certain time sequence, namely, the certain time sequence is non-stationary, under the confidence level of 80%, if the p value of the test result is larger than 0.8, the original assumption is accepted, namely, the time sequences are considered to be non-stationary time sequences, and the subsequent model fitting is feasible and meaningful.
S123, establishing an ARIMA model, and fitting the verification result by using the ARIMA model.
In this embodiment, the ARIMA model is an efficient method of fitting and predicting a non-stationary time series set, but fitting population numbers directly using the ARIMA model results in ignoring other influencing factors. Aiming at the situation, the two are effectively combined, so that the population prediction data of the old can be thinned, the accuracy and the precision of the prediction result are improved, and a better processing effect is achieved.
Specifically, an ARIMA (p, d, q) model is built, taking the population mobility time series set as an example:where d represents the differential order, μ represents the constant term, p represents the autoregressive order, γ represents the autocorrelation coefficient, q represents the moving average order, and γ represents the error term.
And (3) performing ARIMA model fitting on the time sequence set according to the age-specific sexes, and selecting an ARIMA model with the minimum AIC value as a fitting model of the current age-specific sexes according to AIC information criteria, wherein the ARIMA model can be considered to better explain the current data.
S124, performing white noise test on the residual sequence of the ARIMA model to obtain a test result.
In this embodiment, the verification result refers to the result of white noise verification of the residual sequence of the ARIMA model.
Specifically, a residual sequence corresponding to the ARIMA model is obtained through the difference between the true value and the fitting value, white noise test is performed on the residual sequence, a result of receiving the original assumption of white noise is obtained through Box-Ljung test, and otherwise, step S123 is returned.
The original assumption is that a certain sequence is a white noise sequence, under the confidence level of 80%, if the p value of the inspection result is larger than 0.8, the original assumption is accepted, namely the model residual sequence is considered to be irrelevant, and the information contained in the data is fully extracted; otherwise, the residual sequence has correlation, which still contains extractable information, and the original model cannot fit the data well, so that the ARIMA model parameters need to be reselected, and white noise test is performed again on the new model to select a proper model for interpretation of the data.
S125, judging whether the test result receives a white noise original assumption or not;
and S126, if the test result receives the white noise original hypothesis, selecting an ARIMA model with relatively optimal overall prediction effect of population mobility, population mobility and population mortality at each age and each gender by a voting mode, and determining a prediction result of the ARIMA model with relatively optimal prediction effect to obtain a prediction value.
Specifically, population migration is selected by voting according to AIC information criteriaThe ARIMA (p, d, q) model with relatively optimal overall prediction effect of the incidence rate, population migration rate and population mortality of each gender at each age can obtain prediction values as follows:
if the test result does not accept the white noise source hypothesis, the step S123 is performed.
The random forest model is consulted, the overall relatively optimal model is selected through the voting principle, the complexity is reduced, the model is prevented from being too complicated and complicated, and the overall energy consumption is reduced.
S130, calculating population retention according to the predicted value.
In this embodiment, population retention refers to the probability of the population corresponding to survival.
In one embodiment, referring to fig. 5, the step S130 may include steps S131 to S132.
S131, determining a retention prediction function according to the form of the exponential function.
In the present embodiment, the retention prediction function refers to a function for predicting the retention.
S132, inputting the predicted value into the retention prediction function to obtain population retention.
Introducing an exponential function as a retention prediction functionPopulation retention is calculated. Predicted value +.>Substituting to obtain population retention predictive value of corresponding age and sex>
And S140, inputting the initial data and the population retention into an age shifting model to shift the population of a certain age to the next age of the next year under the corresponding probability condition, and predicting the population of the old people to obtain a prediction result.
In the present embodiment, the prediction result refers to a prediction value of the population number of the elderly.
In one embodiment, referring to fig. 7, the step S140 may include steps S141 to S142.
S141, determining population numbers corresponding to ages and sexes in a certain level of area according to the initial data.
In the embodiment, the age and sex population number in a certain level area are obtained according to the historical population data
S142, inputting population numbers corresponding to the ages and sexes in a certain level of area and the population retention rate into an age shift model, calculating population numbers of the ages of the next year of the corresponding sexes, and analogizing the population numbers of the aged specified years after a plurality of years to obtain a prediction result.
In this embodiment, the number of aged people of a given year after several years is Wherein t+n represents n years from the prediction starting point year t to the back, and x represents a designated time; />Representing the population count corresponding to the last year and last age of the gender.
Specifically, the prediction step length is not more than 60 years, and the population quantity is calculated through population retention rate without considering birth factorsI.e. the population number corresponding to the gender of the next year.
Can predict different according to the requirementsAge range population, the population of elderly people 65 years and older in the next year is now predicted as an example:wherein a.gtoreq.64 represents 64 years and older at the predicted starting annual age. Similarly, the number of the aged who are x years old and older after n years can be analogized as follows: />Where t+n represents n years from the predicted starting point year t onwards.
And constructing a retention rate function, introducing an age shifting model, predicting the population quantity of the aged through population retention rate predicted values, improving the prediction accuracy, and taking the prediction result as a unit every year. Meanwhile, the overall relatively optimal model is selected through the voting principle, so that the complexity is reduced, the model is prevented from being too complicated and complicated, and the overall energy consumption is reduced.
For example: acquiring population index data of Zhejiang province of a certain years, aiming at the year t therein m Corresponding missing data D m The completion is carried out by adopting Lagrange interpolation method to As complement values, and generating a time series set of population mobility, population migration rate, population mortality from the complement data. The Phillips-Perron unit root test is carried out on the time sequence set, the test original assumption is that a unit root exists in a certain time sequence, the p value of the test result is larger than 0.8, the original assumption is accepted at the confidence level of 80%, namely, the time sequence is considered to be a non-stable time sequence, and the subsequent model fitting is feasible and meaningful. And (3) performing ARIMA model fitting on the time sequence set according to the age-specific sexes, and selecting an ARIMA model with the minimum AIC value as a fitting model of the current age-specific sexes according to AIC information criteria, wherein the ARIMA model can be considered to better explain the current data. By true valueObtaining a residual sequence corresponding to each model according to the difference value of the fitting values, and carrying out Box-Ljung white noise test on the residual sequence, wherein the original assumption is that a certain sequence is a white noise sequence, if the p value of a test result is greater than 0.8, the original assumption is considered to be accepted under the confidence level of 80%, namely the residual sequence of the model is considered to be irrelevant, and the information contained in the data is fully extracted; otherwise, the residual sequence has correlation, which still contains extractable information, and the original model cannot fit the data well, so that the ARIMA model parameters need to be reselected, and white noise test is performed again on the new model to select a proper model for interpretation of the data. After ARIMA models of different ages and sexes are obtained, voting selection is carried out on models of different time sequence sets to avoid the models from being too complex, and relatively optimal model parameters are selected. According to the selected relative optimal population mobility model, population mobility model and population mortality model, population mobility predicted values and population mortality predicted values of the ages and sexes can be obtained sequentially, population retention predicted values of the ages and sexes are calculated according to the substituted retention function, and population numbers of the ages and sexes are combined and input into an age migration model, so that population number predicted values of the aged 65 years and older in Zhejiang province are obtained.
According to the population quantity prediction method for the aged, historical population data are obtained, and the Lagrange interpolation method is adopted to complement missing data, so that the availability of the data and the reliability of the model are improved. The age interval is shortened, so that the prediction accuracy is greatly improved, and the final prediction result is taken as a unit every year. The overall relative optimal ARIMA model is selected according to the voting principle, so that the complexity is reduced, the model is prevented from being too complicated and complicated, and the overall energy consumption is reduced. Mobility factors are emphasized, mobility, migration rate and death rate are predicted, population retention rate is calculated, and the accuracy of old people prediction is improved.
Fig. 7 is a schematic block diagram of an apparatus 300 for predicting population numbers of elderly people according to an embodiment of the present invention. As shown in fig. 7, the present invention also provides an aged population number prediction apparatus 300 corresponding to the above aged population number prediction method. The aged population number prediction apparatus 300 includes a unit for performing the aged population number prediction method described above, and may be configured in a server. Specifically, referring to fig. 7, the device 300 for predicting the population number of the elderly person includes a data processing unit 301, a fitting prediction unit 302, a retention rate calculation unit 303, and a population prediction unit 304.
The data processing unit 301 is configured to obtain historical population data in a certain level of area, and complement the missing index to obtain initial data; the fitting prediction unit 302 is configured to input the initial data into an ARIMA model to perform fitting and prediction on age, sex population mobility, migration rate and mortality, so as to obtain a predicted value; a retention calculating unit 303, configured to calculate population retention according to the predicted value; the population prediction unit 304 is configured to input the initial data and the population retention rate into an age shift model, so as to shift the population of a certain age in a certain year to a next age in a next year under a corresponding probability condition, and predict the population of the elderly to obtain a prediction result.
In one embodiment, as shown in fig. 8, the data processing unit 301 includes a data acquisition subunit 3011 and a completion subunit 3012.
A data acquisition subunit 3011, configured to acquire historical population data in a certain level of area; and a complementing subunit 3012, configured to complement the index missing in the historical population data by using a lagrangian interpolation method, so as to obtain initial data.
In an embodiment, as shown in fig. 9, the fitting prediction unit 302 includes a time series set generating subunit 3021, a stationarity checking subunit 3022, a fitting subunit 3023, a white noise checking subunit 3024, a judging subunit 3025, and a voting subunit 3026.
A time series set generation subunit 3021 for generating a population time series set according to the initial data; a stationarity checking subunit 3022, configured to perform stationarity checking on the population time series set to obtain a checking result; a fitting subunit 3023, configured to establish an ARIMA model, and fit the verification result by using the ARIMA model; a white noise checking subunit 3024, configured to perform white noise checking on the residual sequence of the ARIMA model to obtain a checking result; a judging subunit 3025 for judging whether the test result accepts a white noise source assumption; and the voting subunit 3026 is configured to select an ARIMA model with relatively optimal overall prediction effect for each gender at each age by voting if the test result receives the white noise source hypothesis, and determine a prediction result of the ARIMA model with relatively optimal prediction effect to obtain a prediction value.
In one embodiment, as shown in fig. 10, the retention calculating unit 303 includes a function determining subunit 3031 and an input subunit 3032.
A function determination subunit 3031, configured to determine a retention prediction function according to the form of the exponential function; an input subunit 3032 is configured to input the predicted value into the retention prediction function to obtain a population retention.
In one embodiment, as shown in fig. 11, the population prediction unit 304 includes a quantity determination subunit 3041 and an aged population prediction subunit 3042.
A quantity determination subunit 3041, configured to determine, according to the initial data, a population quantity corresponding to an age and a gender in a certain level of area; the old people population prediction subunit 3042 is configured to input the population numbers corresponding to the ages and sexes in the certain level of area and the population retention rate into an age shift model, calculate the population numbers corresponding to the ages of the sexes of the next year, and analogize the population numbers of the old people with the specified years after a plurality of years, so as to obtain a prediction result.
It should be noted that, as will be clearly understood by those skilled in the art, the specific implementation process of the above-mentioned population quantity predicting device 300 and each unit of the old people may refer to the corresponding descriptions in the foregoing method embodiments, and for convenience and brevity of description, the description is omitted here.
The above-described senile population quantity predicting apparatus 300 may be implemented in the form of a computer program which can be run on a computer device as shown in fig. 12.
Referring to fig. 12, fig. 12 is a schematic block diagram of a computer device according to an embodiment of the present application. The computer device 500 may be a server, where the server may be a stand-alone server or may be a server cluster formed by a plurality of servers.
With reference to FIG. 12, the computer device 500 includes a processor 502, memory, and a network interface 505 connected by a system bus 501, where the memory may include a non-volatile storage medium 503 and an internal memory 504.
The non-volatile storage medium 503 may store an operating system 5031 and a computer program 5032. The computer program 5032 includes program instructions that, when executed, cause the processor 502 to perform a method of population count prediction for elderly people.
The processor 502 is used to provide computing and control capabilities to support the operation of the overall computer device 500.
The internal memory 504 provides an environment for the execution of a computer program 5032 in the non-volatile storage medium 503, which computer program 5032, when executed by the processor 502, causes the processor 502 to perform a method for predicting the population of elderly people.
The network interface 505 is used for network communication with other devices. Those skilled in the art will appreciate that the architecture shown in fig. 12 is merely a block diagram of a portion of the architecture in connection with the present application and is not intended to limit the computer device 500 to which the present application is applied, and that a particular computer device 500 may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.
Wherein the processor 502 is configured to execute a computer program 5032 stored in a memory to implement the steps of:
acquiring historical population data in a certain level of area, and complementing the missing index to obtain initial data; inputting the initial data into an ARIMA model to perform fitting and prediction on age, sex population mobility, migration rate and death rate so as to obtain a predicted value; calculating population retention according to the predicted value; and inputting the initial data and the population retention into an age shifting model to shift the population of a certain age to the next age of the next year under the corresponding probability condition, and predicting the population of the old people to obtain a prediction result.
In one embodiment, when the processor 502 performs the step of obtaining the historical population data in the certain level of area and complementing the missing index to obtain the initial data, the following steps are specifically implemented:
acquiring historical population data in a certain level of area; and complementing the indexes missing in the historical population data by adopting a Lagrange interpolation method to obtain initial data.
In one embodiment, when the step of inputting the initial data into the ARIMA model to perform the fitting and prediction of age, sex population mobility, migration rate and mortality to obtain the predicted value, the processor 502 specifically performs the following steps:
Generating a population time series set according to the initial data; performing stationarity test on the population time series set to obtain a verification result; establishing an ARIMA model, and fitting the verification result by using the ARIMA model; performing white noise test on the residual sequence of the ARIMA model to obtain a test result; judging whether the test result receives a white noise original hypothesis or not; if the test result receives the white noise source hypothesis, selecting an ARIMA model with relatively optimal overall prediction effect of population mobility, population mobility and population mortality at each age and each gender by voting, and determining a prediction result of the ARIMA model with relatively optimal prediction effect to obtain a prediction value.
Wherein the population time series set comprises a population mobility time series set, a population mobility time series set and a population mortality time series set.
In one embodiment, when the step of calculating the population retention according to the predicted value is implemented by the processor 502, the following steps are specifically implemented:
determining a retention prediction function according to the form of the exponential function; and inputting the predicted value into the retention prediction function to obtain the population retention.
In one embodiment, when the processor 502 performs the step of inputting the initial data and the population retention rate into the age-shifting model to shift the population of a certain age to a next age of a certain year under the corresponding probability condition, and performs the prediction of the population of the elderly to obtain the prediction result, the following steps are specifically implemented:
determining population numbers corresponding to ages and sexes in a certain level of area according to the initial data; and inputting the population quantity corresponding to the ages and sexes in a certain level of region and the population retention rate into an age shift model, calculating the population quantity of the next age of the next year corresponding to the sexes, and analogizing the population quantity of the aged with the designated years after a plurality of years to obtain a prediction result.
Wherein the number of the aged population with specified years after a plurality of years isWherein t+n represents n years from the prediction starting point year t to the back, and x represents a designated time; />Representing the population count corresponding to the last year and last age of the gender.
It should be appreciated that in embodiments of the present application, the processor 502 may be a central processing unit (Central Processing Unit, CPU), the processor 502 may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSPs), application specific integrated circuits (Application Specific Integrated Circuit, ASICs), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. Wherein the general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
Those skilled in the art will appreciate that all or part of the flow in a method embodying the above described embodiments may be accomplished by computer programs instructing the relevant hardware. The computer program comprises program instructions, and the computer program can be stored in a storage medium, which is a computer readable storage medium. The program instructions are executed by at least one processor in the computer system to implement the flow steps of the embodiments of the method described above.
Accordingly, the present invention also provides a storage medium. The storage medium may be a computer readable storage medium. The storage medium stores a computer program which, when executed by a processor, causes the processor to perform the steps of:
acquiring historical population data in a certain level of area, and complementing the missing index to obtain initial data; inputting the initial data into an ARIMA model to perform fitting and prediction on age, sex population mobility, migration rate and death rate so as to obtain a predicted value; calculating population retention according to the predicted value; and inputting the initial data and the population retention into an age shifting model to shift the population of a certain age to the next age of the next year under the corresponding probability condition, and predicting the population of the old people to obtain a prediction result.
In one embodiment, when the processor executes the computer program to obtain the historical population data in the certain level of area and complement the missing index to obtain the initial data, the method specifically comprises the following steps:
acquiring historical population data in a certain level of area; and complementing the indexes missing in the historical population data by adopting a Lagrange interpolation method to obtain initial data.
In one embodiment, when the processor executes the computer program to implement the step of inputting the initial data into an ARIMA model to perform fitting and prediction on age, sex population mobility, migration rate and mortality to obtain a predicted value, the method specifically includes the following steps:
generating a population time series set according to the initial data; performing stationarity test on the population time series set to obtain a verification result; establishing an ARIMA model, and fitting the verification result by using the ARIMA model; performing white noise test on the residual sequence of the ARIMA model to obtain a test result; judging whether the test result receives a white noise original hypothesis or not; if the test result receives the white noise source hypothesis, selecting an ARIMA model with relatively optimal overall prediction effect of population mobility, population mobility and population mortality at each age and each gender by voting, and determining a prediction result of the ARIMA model with relatively optimal prediction effect to obtain a prediction value.
Wherein the population time series set comprises a population mobility time series set, a population mobility time series set and a population mortality time series set.
In one embodiment, when the processor executes the computer program to implement the step of calculating population retention according to the predicted value, the processor specifically implements the following steps:
determining a retention prediction function according to the form of the exponential function; and inputting the predicted value into the retention prediction function to obtain the population retention.
In one embodiment, when the processor executes the computer program to implement the step of inputting the population data and the population retention rate according to the initial data into an age shifting model to shift the population count of a certain age to a next age of a next year under a corresponding probability condition, and predict the population count of the elderly to obtain a predicted result, the method specifically includes the following steps:
determining population numbers corresponding to ages and sexes in a certain level of area according to the initial data; and inputting the population quantity corresponding to the ages and sexes in a certain level of region and the population retention rate into an age shift model, calculating the population quantity of the next age of the next year corresponding to the sexes, and analogizing the population quantity of the aged with the designated years after a plurality of years to obtain a prediction result.
Wherein the number of the aged population with specified years after a plurality of years isWherein t+n represents n years from the prediction starting point year t to the back, and x represents a designated time; />Representing the population count corresponding to the last year and last age of the gender.
The storage medium may be a U-disk, a removable hard disk, a Read-Only Memory (ROM), a magnetic disk, or an optical disk, or other various computer-readable storage media that can store program codes.
Those of ordinary skill in the art will appreciate that the elements and algorithm steps described in connection with the embodiments disclosed herein may be embodied in electronic hardware, in computer software, or in a combination of the two, and that the elements and steps of the examples have been generally described in terms of function in the foregoing description to clearly illustrate the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the several embodiments provided by the present invention, it should be understood that the disclosed apparatus and method may be implemented in other manners. For example, the device embodiments described above are merely illustrative. For example, the division of each unit is only one logic function division, and there may be another division manner in actual implementation. For example, multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed.
The steps in the method of the embodiment of the invention can be sequentially adjusted, combined and deleted according to actual needs. The units in the device of the embodiment of the invention can be combined, divided and deleted according to actual needs. In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The integrated unit may be stored in a storage medium if implemented in the form of a software functional unit and sold or used as a stand-alone product. Based on such understanding, the technical solution of the present invention is essentially or a part contributing to the prior art, or all or part of the technical solution may be embodied in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a terminal, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention.
While the invention has been described with reference to certain preferred embodiments, it will be understood by those skilled in the art that various changes and substitutions of equivalents may be made and equivalents will be apparent to those skilled in the art without departing from the scope of the invention. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
Claims (7)
1. A method for predicting population of elderly people, comprising:
acquiring historical population data in a certain level of area, and complementing the missing index to obtain initial data;
inputting the initial data into an ARIMA model to perform fitting and prediction on age, sex population mobility, migration rate and death rate so as to obtain a predicted value;
calculating population retention according to the predicted value;
inputting the initial data and population retention into an age shifting model to shift population numbers of a certain age to a next age of a next year under the corresponding probability condition, and predicting population numbers of the old people to obtain a prediction result;
the initial data is input into an ARIMA model to be fitted and predicted for age, sex population mobility, migration rate and death rate to obtain a predicted value, and the method comprises the following steps:
generating a population time series set according to the initial data;
performing stationarity test on the population time series set to obtain a verification result;
establishing an ARIMA model, fitting the verification result by using the ARIMA model, and establishing an ARIMA (p, d, q) model, wherein the population mobility time sequence set is as follows: Wherein d represents the differential order, μ represents the constant term, p represents the autoregressive order, γ represents the autocorrelation coefficient, q represents the moving average order, and ε represents the error term;
performing white noise test on the residual sequence of the ARIMA model to obtain a test result;
judging whether the test result receives a white noise original hypothesis or not;
if the test result receives the white noise original assumption, selecting an ARIMA model with relatively optimal overall prediction effect of each gender at each age according to population mobility, population mobility and population mortality in a voting mode, and determining a prediction result of the ARIMA model with relatively optimal prediction effect to obtain a prediction value;
performing ARIMA model fitting on the time sequence set according to age-based sexes, and selecting an ARIMA model with the minimum AIC value as a fitting model of the current age-based sexes according to AIC information criteria, wherein the ARIMA model can be considered to better explain the current data;
inputting the initial data and population retention into an age shifting model to shift population of a certain age to a next age of a next year under a corresponding probability condition, and predicting population of the elderly to obtain a prediction result, wherein the method comprises the following steps of:
Determining population numbers corresponding to ages and sexes in a certain level of area according to the initial data;
inputting population numbers corresponding to the ages and sexes in a certain level of region and the population retention rate into an age shift model, calculating population numbers of the ages of the next year of the corresponding sexes, and analogizing the population numbers of the aged specified years after a plurality of years to obtain a prediction result;
the number of aged people with specified years after several years isWherein t+n represents n years from the prediction starting point year t to the back, and x represents a designated time; />Representing the population count corresponding to the last year and last age of the gender.
2. The method of claim 1, wherein the step of obtaining historical population data in a certain class of areas and complementing missing indicators to obtain initial data comprises:
acquiring historical population data in a certain level of area;
and complementing the indexes missing in the historical population data by adopting a Lagrange interpolation method to obtain initial data.
3. The method of claim 1, wherein the population time series set comprises a population mobility time series set, and a population mortality time series set.
4. The method of claim 1, wherein calculating population retention based on the predicted value comprises:
determining a retention prediction function according to the form of the exponential function;
and inputting the predicted value into the retention prediction function to obtain the population retention.
5. Old person's mouth quantity prediction device, its characterized in that includes:
the data processing unit is used for acquiring historical population data in a certain level of area and complementing the missing indexes to obtain initial data;
the fitting prediction unit is used for inputting the initial data into an ARIMA model to perform fitting and prediction on age, sex population mobility, migration rate and death rate so as to obtain a predicted value;
the retention rate calculation unit is used for calculating population retention rate according to the predicted value;
the population prediction unit is used for inputting the initial data and the population retention into an age shifting model so as to shift the population of a certain age to the next age of the next year under the corresponding probability condition, and predicting the population of the old people so as to obtain a prediction result;
the fitting prediction unit comprises a time sequence set generation subunit, a stationarity check subunit, a fitting subunit, a white noise check subunit, a judgment subunit and a voting subunit;
A time sequence set generating subunit, configured to generate a population time sequence set according to the initial data; the stability checking subunit is used for carrying out stability checking on the population time sequence set to obtain a checking result; the fitting subunit is used for establishing an ARIMA model and fitting the verification result by utilizing the ARIMA model; a white noise checking subunit, configured to perform white noise checking on the residual sequence of the ARIMA model to obtain a checking result; a judging subunit, configured to judge whether the test result accepts a white noise original assumption; the voting subunit is used for selecting an ARIMA model with relatively optimal overall prediction effect of each gender of each age according to population mobility, population migration rate and population mortality in a voting mode if the test result receives the white noise original assumption, and determining a prediction result of the ARIMA model with relatively optimal prediction effect to obtain a prediction value;
establishing an ARIMA (p, d, q) model, wherein the population mobility time series set is as follows: wherein d represents the differential order, μ represents the constant term, p represents the autoregressive order, γ represents the autocorrelation coefficient, q represents the moving average order, and ε represents the error term;
Inputting the initial data and population retention into an age shifting model to shift population of a certain age to a next age of a next year under a corresponding probability condition, and predicting population of the elderly to obtain a prediction result, wherein the method comprises the following steps of:
determining population numbers corresponding to ages and sexes in a certain level of area according to the initial data;
inputting population numbers corresponding to the ages and sexes in a certain level of region and the population retention rate into an age shift model, calculating population numbers of the ages of the next year of the corresponding sexes, and analogizing the population numbers of the aged specified years after a plurality of years to obtain a prediction result;
the number of aged people with specified years after several years isWherein t+n represents n years from the prediction starting point year t to the back, and x represents a designated time; />Representing the population count corresponding to the last year and last age of the gender.
6. A computer device, characterized in that it comprises a memory on which a computer program is stored and a processor which, when executing the computer program, implements the method according to any of claims 1-4.
7. A storage medium storing a computer program which, when executed by a processor, implements the method of any one of claims 1 to 4.
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