Real estate market heat index prediction method based on spatiotemporal correlation of multi-source data

Abstract

The present invention relates to a real estate market heat index prediction method based on the spatiotemporal association of multi-source data. By pre-forming a multi-source indicator system that characterizes the laws of urban real estate markets, a pre-processed multi-source indicator data set of all cities within a national administrative region, a real estate heat index of each city, and a logarithmic change rate of real estate heat are obtained. A denoised cross-correlation matrix of the real estate heat index at different times is calculated. A planar maximum filtered graph obtained from the denoised cross-correlation matrix is ​​used as a real estate heat association network. The real estate heat association network and a time series of real estate heat indices of cities within the national administrative region are input into a temporal graph convolutional network for processing. The temporal graph convolutional network outputs a predicted value of the real estate market heat index of each city. This method uses the temporal graph convolutional network to capture the spatial and temporal dependencies of real estate markets in different cities, and more comprehensively and accurately predicts the real estate market heat indices of different cities.

Description

Technical Field

[0001] The present invention relates to the field of real estate, and in particular to a method for predicting a real estate market heat index based on spatiotemporal correlation of multi-source data. Background Art

[0002] The real estate market is a pillar of economic growth, and its development drives the prosperity of fundamental sectors of the national economy, including construction, industry, commerce, and transportation. However, if the real estate market becomes disorderly or structurally unbalanced, it will threaten the healthy development of the macroeconomy.

[0003] To keep abreast of real estate trends, market heat is often used to describe the growth rate of various real estate market indicators. Accurately predicting market heat is crucial for understanding the real estate market.

[0004] However, the existing real estate market heat index prediction method has shortcomings: since the heat of the real estate market is affected by multiple factors and the mutual influence between these multiple factors, the existing real estate market heat index prediction method is usually based on a single indicator, and cannot form a systematic multi-source indicator system that characterizes the regular characteristics of the real estate market, resulting in difficulty in more comprehensive and accurate prediction of the real estate market heat index. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a real estate market heat index prediction method based on the spatiotemporal correlation of multi-source data in response to the above-mentioned existing technology.

[0006] The technical solution adopted by the present invention to solve the above technical problems is: a real estate market heat index prediction method based on spatiotemporal correlation of multi-source data, characterized by comprising the following steps:

[0007] Step 1: Pre-form a multi-source indicator system that characterizes the laws of the urban real estate market; wherein the multi-source indicator system includes the transaction price of new homes, the transaction volume of new home sales, the transaction price of pre-owned homes, the transaction volume of pre-owned homes, the transaction price of land, the area of ​​land transferred, the transaction price of house rentals, and a market sentiment value within the city. The market sentiment value is obtained by processing news text data related to the real estate market within the city; the city is a municipality directly under the central government or a prefecture-level city within the national administrative region;

[0008] Step 2: Obtain a multi-source indicator data set for all cities within the national administrative region based on the multi-source indicator system, and perform preprocessing on the multi-source indicator data set to remove abnormal data to obtain a preprocessed multi-source indicator data set;

[0009] Among them, the indicator types in the multi-source indicator data set correspond one-to-one to the indicator types in the multi-source indicator system. The multi-source indicator data set includes the indicator data corresponding to different cities at different times. The multi-source indicator data set after preprocessing is marked as X, X = {X i,p (t)};X i,p (t) is the i-th indicator data X of the p-th city in the preprocessed multi-source indicator data set X i The preprocessed value at time t, 1≤p≤M, 1≤i≤N; M is the total number of cities in the national administrative area, and N is the total number of indicator data types in the multi-source indicator system;

[0010] Step 3: normalize all preprocessed data in the preprocessed multi-source indicator data set to obtain a normalized multi-source indicator data set, and calculate the indicator weight of each indicator data in the normalized multi-source indicator data set at different times and the entropy value of each indicator data;

[0011] Step 4: Calculate the difference coefficient and corresponding weight value of each indicator data based on the entropy value of each indicator data, and calculate the real estate heat index of each city based on the obtained weight values;

[0012] Step 5: Calculate the real estate heat index of all cities within the national administrative region at different times, and based on the obtained real estate heat index, obtain the logarithmic change rate of real estate heat of each city within a preset time period;

[0013] Step 6: Based on the obtained logarithmic change rate of real estate heat in each city, calculate the cross-correlation matrix representing the real estate heat index of all cities within the national administrative region at different times;

[0014] Step 7, performing random decomposition processing on the cross-correlation matrix to obtain decomposed matrix elements, and performing denoising processing on the decomposed matrix elements to obtain a denoised cross-correlation matrix;

[0015] Step 8: Based on the obtained denoised cross-correlation matrix, construct a planar maximum filter graph of the denoised cross-correlation matrix, and use the planar maximum filter graph as a real estate heat correlation network representing the mutual correlation between all cities within the national administrative region;

[0016] In step 9, the obtained real estate heat association network and the time series of the real estate heat index of each city within the national administrative region are used as input and input into the time graph convolutional network for processing, and the output result of the time graph convolutional network processing is used as the predicted value of the real estate market heat index of each city within the national administrative region.

[0017] Improved, in the real estate market heat index prediction method based on spatiotemporal correlation of multi-source data, the abnormal data includes duplicate data and missing data.

[0018] Furthermore, in the real estate market heat index prediction method based on spatiotemporal correlation of multi-source data, in step 3, the normalization process is performed to obtain the normalized multi-source indicator data set as follows:

[0019]

[0020] The normalized multi-source indicator data set is labeled as x, where x={x i,p (t)};x i,p (t) represents the i-th indicator data X of the p-th city i,p The normalized data value at time t, max(X i,p ) represents the i-th indicator data X of the p-th city i,p The maximum pre-processed data value in the pre-processed multi-source indicator data set X, min(X i,p ) represents the i-th indicator data X of the p-th city i,p The minimum preprocessed data value in the preprocessed multi-source indicator data set X.

[0021] Improved, in the real estate market heat index prediction method based on spatiotemporal correlation of multi-source data, in step 3, the index weights of the index data at different times and the entropy values ​​of the index data are calculated as follows:

[0022]

[0023] Among them, U i,p (t) is the i-th indicator data X of the p-th city i,p The weight of the indicator at time t; is the normalized value x i,p (t) after the self-increment processing of the index data value, Δ i is a positive number infinitely close to zero;

[0024]

[0025] Among them, e i,p is the entropy value of the i-th indicator data of the p-th city; Represents the i-th indicator data X of the p-th city i,p The self in the time period [t1,t m ] The corresponding indicator weight U i,p (t) The total number of

[0026] Further improved, in the real estate market heat index prediction method based on spatiotemporal correlation of multi-source data, in step 4, the difference coefficient of each indicator data and the real estate heat index of each city are calculated as follows:

[0027] g i,p =1-e i,p ;

[0028]

[0029] Among them, g i,p is the i-th indicator data X of the p-th city i,p The coefficient of variation, W i,p is the i-th indicator data X of the p-th city i,p The weight value of S p (t) is the real estate heat index of the p-th city at time t.

[0030] Furthermore, in the real estate market heat index prediction method based on spatiotemporal correlation of multi-source data, in step 5, the logarithmic change rate of the real estate heat of the city within the preset time period is calculated as follows:

[0031]

[0032] H p (t) = lnS p (t+Δt)-lnS p (t); 1≤p≤M;

[0033] Among them, h p (t) represents the logarithmic change rate of real estate popularity in the p-th city within the preset time period Δt, <H p (t) represents the average value of the sum of the real estate heat index of the p-th city within the preset time period Δt, σ p is the standard deviation of the real estate heat index of the p-th city within the preset time period Δt; the preset time period is a multiple of one day.

[0034] Further improved, in the real estate market heat index prediction method based on spatiotemporal correlation of multi-source data, in step 6, the cross-correlation matrix is ​​calculated as follows:

[0035] C pq = <h P (t)·h q (t)>;1≤q≤M;

[0036] The cross-correlation matrix is ​​marked as C, which is a real symmetric matrix. The matrix elements on the diagonal of the real symmetric matrix are all 1, and the matrix elements on the non-diagonal of the real symmetric matrix have a value range of [-1, 1]; C pq is a matrix element of the cross-correlation matrix C, the matrix element C pq represents the strength of the correlation effect between city p and city q on real estate popularity, h q (t) shows the logarithmic change rate of real estate heat in the qth city within the preset time period Δt.

[0037] Furthermore, in the real estate market heat index prediction method based on spatiotemporal correlation of multi-source data, in step 7, the matrix elements obtained after the decomposition process are processed as follows:

[0038]

[0039] Among them, λ α Represents the matrix element C pq The αth eigenvalue of , K is the total number of elements in the eigenvector corresponding to the αth eigenvalue, represents the pth element in the eigenvector corresponding to the αth eigenvalue; represents the qth element in the eigenvector corresponding to the αth eigenvalue; Represents the cross-correlation value between the p-th element and the q-th element under the eigenvector corresponding to the α-th eigenvalue;

[0040] In step 7, the denoised cross-correlation matrix is ​​obtained as follows:

[0041]

[0042] Among them, P r (λ) is the probability distribution function of the eigenvalue λ, is the maximum value of eigenvalue λ, is the minimum value of the eigenvalue λ, T is the length of the random time series, and N is the total number of random time series with length T.

[0043] Furthermore, in the real estate market heat index prediction method based on spatiotemporal association of multi-source data, in step 9, the real estate market heat index prediction value of each city within the national administrative region is obtained as follows:

[0044] f(X,A)=σ(RELU(A * XW0)W1); and f(X,A)∈R N×T' ;

[0045] W0∈R P×H ;W1∈RH×T' ;

[0046] Among them, f(X,A) represents the output of the temporal graph convolutional network GCN when the input is X and A, X is the feature matrix, A is the real estate heat association network as the adjacency matrix, A * represents the preprocessed matrix of the adjacency matrix A; W0 represents the weight matrix from the input layer to the hidden layer, P is the length of the feature matrix X, and H is the number of hidden units in the hidden layer; W1 represents the weight matrix from the hidden layer to the output layer, T' represents the prediction length, σ represents the activation function, and RELU represents the nonlinear activation function in the temporal graph convolutional network GCN.

[0047] Compared with the existing technology, the advantages of the present invention are: the real estate market heat index prediction method based on the spatiotemporal correlation of multi-source data of the present invention forms a multi-source indicator system to comprehensively characterize the investment intentions of real estate market entities and their expectations of market trends, and obtains a multi-source indicator data set and a pre-processed multi-source indicator data set for all cities in the national administrative region based on the multi-source indicator system, and then normalizes the pre-processed multi-source indicator data set, calculates the weights of different indicators, calculates the entropy values ​​of different indicator data, and calculates the indicator data difference coefficient and the indicator data weight value, and then calculates the real estate heat index and the logarithmic change rate of real estate heat for each city, and then calculates the real estate heat index of all cities in the national administrative region based on the logarithmic change rate of real estate heat in each city. The cross-correlation matrix of the real estate heat index at different times is obtained, and the denoised cross-correlation matrix is ​​obtained for the cross-correlation matrix. The planar maximum filtered graph obtained from the denoised cross-correlation matrix is ​​then used as the real estate heat correlation network. The real estate heat correlation network and the time series of the real estate heat index of each city in the national administrative region are input into the temporal graph convolutional network for processing. The output results of the temporal graph convolutional network are used as the predicted values ​​of the real estate market heat index of each city in the national administrative region. The temporal graph convolutional network is used to capture the spatial and temporal dependence of the real estate markets of different cities, thereby taking into account the impact of surrounding adjacent cities on the real estate market of the target city, making the prediction of the real estate market heat index of different cities more comprehensive and accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 2 is a flow chart of a method for predicting real estate market heat index based on spatiotemporal correlation of multi-source data in an embodiment of the present invention. DETAILED DESCRIPTION

[0049] The present invention will be described in further detail below with reference to the accompanying drawings and embodiments.

[0050] This embodiment provides a method for predicting the real estate market heat index based on the spatiotemporal correlation of multi-source data. Figure 1 As shown, the real estate market heat index prediction method based on spatiotemporal correlation of multi-source data includes the following steps:

[0051] Step 1: Pre-form a multi-source indicator system that characterizes the laws of the urban real estate market; wherein the multi-source indicator system includes the transaction price of new homes, the transaction volume of new home sales, the transaction price of pre-owned homes, the transaction volume of pre-owned homes, the transaction price of land, the area of ​​land transferred, the transaction price of house rentals, and a market sentiment value within the city. The market sentiment value is obtained by processing news text data related to the real estate market within the city; the city is a municipality directly under the central government or a prefecture-level city within the national administrative region;

[0052] Specifically, in this embodiment, the specific calculation method of "market sentiment value" is: using the "Jieba" tool ( https: / / github.com / fxsjy / jieba) This method can reliably extract positive and negative emotions from short texts, thereby quantifying the emotions in the text. Specifically, using an artificially generated Chinese sentiment vocabulary dictionary, a dictionary-based method matches sentiment words to determine the emotional tendency of the text. The emotion of each text is represented by two values, s + and s - Corresponding to positive and negative emotions respectively, the sentiment values ​​of all texts every day are summed up to obtain the time series of market sentiment values;

[0053] Step 2: Obtain a multi-source indicator data set for all cities within the national administrative region based on the multi-source indicator system, and perform preprocessing on the multi-source indicator data set to remove abnormal data to obtain a preprocessed multi-source indicator data set;

[0054] Among them, the indicator types in the multi-source indicator data set correspond one-to-one to the indicator types in the multi-source indicator system. The multi-source indicator data set includes the indicator data corresponding to different cities at different times. The multi-source indicator data set after preprocessing is marked as X, X = {X i,p (t)};X i,p (t) is the i-th indicator data X of the p-th city in the preprocessed multi-source indicator data set X i The preprocessed value at time t, 1≤p≤M, 1≤i≤N; M is the total number of cities in the national administrative area, and N is the total number of indicator data types in the multi-source indicator system; abnormal data here include duplicate data and missing data;

[0055] Step 3: normalize all preprocessed data in the preprocessed multi-source indicator data set to obtain a normalized multi-source indicator data set, and calculate the indicator weight of each indicator data in the normalized multi-source indicator data set at different times and the entropy value of each indicator data;

[0056] Specific to this embodiment:

[0057] The normalization process to obtain the normalized multi-source indicator data set is as follows:

[0058]

[0059] Among them, the normalized multi-source indicator data set is marked as x, x={x i,p (t)};x i,p (t) represents the i-th indicator data X of the p-th city i,p The normalized data value at time t, max(X i,p ) represents the i-th indicator data X of the p-th city i,p The maximum pre-processed data value in the pre-processed multi-source indicator data set X, min(X i,p ) represents the i-th indicator data X of the p-th city i,p The minimum pre-processed data value in the pre-processed multi-source indicator data set X;

[0060] The indicator weights of each indicator data at different times and the entropy value calculation methods of each indicator data are as follows:

[0061]

[0062] Among them, U i,p (t) is the i-th indicator data X of the p-th city i,p The weight of the indicator at time t; is the normalized value x i,p (t) after the self-increment processing of the index data value, Δ i is a positive number infinitely close to zero;

[0063]

[0064] Among them, e i,p is the entropy value of the i-th indicator data of the p-th city; Represents the i-th indicator data X of the p-th city i,p The self in the time period [t1,t m ] The corresponding indicator weight U i,p (t) the total number;

[0065] Step 4: Calculate the difference coefficient and corresponding weight value of each indicator data based on the entropy value of each indicator data, and calculate the real estate heat index of each city based on the obtained weight values;

[0066] Specifically in this embodiment, the difference coefficient of each indicator data and the calculation method of the real estate heat index of each city are as follows:

[0067] g i,p =1-e i,p ;

[0068]

[0069] Among them, g i,p is the i-th indicator data X of the p-th city i,p The coefficient of variation, W i,p is the i-th indicator data X of the p-th city i,p The weight value of S p (t) is the real estate heat index of the p-th city at time t;

[0070] Step 5: Calculate the real estate heat index of all cities within the national administrative region at different times, and obtain the logarithmic rate of change of real estate heat for each city within a preset time period based on the obtained real estate heat index. In this embodiment, the logarithmic rate of change of real estate heat for a city within a preset time period is calculated as follows:

[0071]

[0072] H p (t) = lnS p (t+Δt)-lnS p (t); 1≤p≤M;

[0073] Among them, h p (t) represents the logarithmic change rate of real estate popularity in the p-th city within the preset time period Δt, <H p (t) represents the average value of the sum of the real estate heat index of the p-th city within the preset time period Δt, σ p is the standard deviation of the real estate heat index of the p-th city within the preset time period Δt; the preset time period is a multiple of one day, for example, the preset time period is one month, one quarter, or one year;

[0074] Step 6: Based on the obtained logarithmic change rate of real estate heat in each city, calculate the cross-correlation matrix representing the real estate heat index of all cities in the national administrative region at different times. In step 6, the cross-correlation matrix is ​​calculated as follows:

[0075] C pq = <h P (t)·h q (t)>;1≤q≤M;

[0076] The cross-correlation matrix is ​​marked as C. The cross-correlation matrix C is a real symmetric matrix. The matrix elements on the diagonal of the real symmetric matrix are all 1, and the matrix elements on the non-diagonal of the real symmetric matrix have a value range of [-1, 1]. pq is a matrix element of the cross-correlation matrix C, the matrix element C pq represents the strength of the correlation effect between city p and city q on real estate popularity, h q (t) shows the logarithmic change rate of real estate popularity in the qth city within the preset time period Δt;

[0077] Step 7, performing random decomposition processing on the cross-correlation matrix to obtain decomposed matrix elements, and performing denoising processing on the decomposed matrix elements to obtain a denoised cross-correlation matrix;

[0078] Specifically, in this embodiment, the manner of obtaining the matrix element after the decomposition process is as follows:

[0079]

[0080] Among them, λ α Represents the matrix element C pq The αth eigenvalue of represents the pth element in the αth eigenvector; represents the qth element in the αth eigenvector; Characterize the cross-correlation value between the p-th element and the q-th element in the α-th characteristic mode;

[0081] In step 7, the denoised cross-correlation matrix is ​​obtained as follows:

[0082]

[0083] Among them, P r (λ) is the probability distribution function of the eigenvalue λ, is the maximum value of eigenvalue λ, is the minimum value of the eigenvalue λ, T is the length of the random time series, and N is the total number of random time series with length T;

[0084] The above-mentioned random time series refers to a sequence composed of random variables, which are arranged in chronological order. In this sequence, each random variable represents an observation or measurement value at a specific point in time. It meets the following two conditions: first, constant mean, for all time points, the expected value (i.e., mean) of the random variable is constant; second, constant variance, for all time points, the variance (i.e., variability or dispersion) of the random variable is constant and does not change with time;

[0085] Step 8: Based on the obtained denoised cross-correlation matrix, a maximum plane filter graph of the denoised cross-correlation matrix is ​​constructed, and the maximum plane filter graph is used as a real estate popularity correlation network representing the mutual correlation effects between all cities within the national administrative region. The method of constructing a maximum plane filter graph based on a matrix is ​​a mature technical means and will not be repeated here.

[0086] Step 9: The obtained real estate heat association network and the time series of real estate heat index of each city within the national administrative region are input into the temporal graph convolutional network for processing, and the output of the temporal graph convolutional network is used as the predicted value of the real estate market heat index of each city within the national administrative region. The predicted value of the real estate market heat index of each city within the national administrative region is obtained as follows:

[0087] f(X,A)=σ(RELU(A * XW0)W1); and f(X,A)∈R N×T' ;

[0088] W0∈R P×H ;W1∈R H×T' ;

[0089] Among them, f(X,A) represents the output of the temporal graph convolutional network GCN when the input is X and A, X is the feature matrix, A is the real estate heat association network as the adjacency matrix, A * represents the preprocessed matrix of the adjacency matrix A; W0 represents the weight matrix from the input layer to the hidden layer, P is the length of the feature matrix X, and H is the number of hidden units in the hidden layer; W1 represents the weight matrix from the hidden layer to the output layer, T' represents the prediction length, σ represents the activation function, and RELU represents the nonlinear activation function in the temporal graph convolutional network GCN.

[0090] In the real estate market heat index prediction method based on spatiotemporal correlation of multi-source data in this embodiment, a multi-source indicator system that characterizes the laws of the urban real estate market is formed in advance to comprehensively characterize the investment intentions of real estate market entities and their expectations of market trends. Based on the multi-source indicator system, a multi-source indicator data set of all cities in the national administrative region is obtained, and a pre-processed multi-source indicator data set is further obtained. The pre-processed multi-source indicator data set is then normalized, weights of different indicators are calculated, entropy values ​​of different indicator data are calculated, and indicator data difference coefficients and indicator data weight values ​​are calculated. Based on the obtained weight values, the real estate heat index of each city is calculated respectively, and based on the obtained real estate heat index, the logarithmic change rate of the real estate heat of each city in a preset time period is obtained respectively. Based on the real estate heat index of each city, the real estate heat index of each city is calculated. The logarithmic rate of change of degree is used to calculate the cross-correlation matrix of the real estate heat index of all cities in the national administrative area at different times, and a denoised cross-correlation matrix is ​​obtained for the cross-correlation matrix. The planar maximum filtered graph based on the denoised cross-correlation matrix is ​​then used as the real estate heat correlation network. The obtained real estate heat correlation network and the time series of the real estate heat index of each city in the national administrative area are used as input and input into the temporal graph convolutional network for processing. The output result of the temporal graph convolutional network is used as the predicted value of the real estate market heat index of each city in the national administrative area. The temporal graph convolutional network is used to capture the spatial and temporal dependence of the real estate markets of different cities, thereby taking into account the impact of surrounding adjacent cities on the real estate market of the target city, thereby making the prediction of the real estate market heat index of different cities more accurate.

[0091] Although the preferred embodiments of the present invention have been described in detail above, it should be clearly understood that various modifications and variations are possible for those skilled in the art. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A real estate market heat index prediction method based on spatiotemporal correlation of multi-source data, characterized by: The steps include: Step 1: preform a multi-source indicator system that characterizes the laws of the urban real estate market; wherein, the multi-source indicator system includes the transaction price of new houses, the transaction volume of new house sales, the transaction price of second-hand houses, the transaction volume of second-hand houses, the transaction price of land, the land transfer area, the transaction price of house rentals, and the market sentiment value in the city. The market sentiment value is obtained by processing news text data related to the real estate market in the city; the city is a municipality or a prefecture-level city within the national administrative area; and the specific calculation method of the market sentiment value is: using an artificially generated Chinese sentiment vocabulary dictionary, the dictionary-based method matches the sentiment words to determine the sentiment tendency of the text; the sentiment of each text is represented by two values, s + and s - Corresponding to positive and negative emotions respectively, the sentiment values ​​of all texts every day are summed up to obtain the time series of market sentiment values; Step 2: Obtain a multi-source indicator data set for all cities within the national administrative region based on the multi-source indicator system, and perform preprocessing on the multi-source indicator data set to remove abnormal data to obtain a preprocessed multi-source indicator data set; Among them, the indicator types in the multi-source indicator data set correspond one-to-one to the indicator types in the multi-source indicator system. The multi-source indicator data set includes the indicator data corresponding to different cities at different times. The multi-source indicator data set after preprocessing is marked as X, X = {X i,p (t)};X i,p (t) is the i-th indicator data X of the p-th city in the preprocessed multi-source indicator data set X i The preprocessed value at time t, 1≤p≤M, 1≤i≤N; M is the total number of cities in the national administrative area, and N is the total number of indicator data types in the multi-source indicator system; Step 3: normalize all preprocessed data in the preprocessed multi-source indicator data set to obtain a normalized multi-source indicator data set, and calculate the indicator weight of each indicator data in the normalized multi-source indicator data set at different times and the entropy value of each indicator data; Step 4: Calculate the difference coefficient and corresponding weight value of each indicator data based on the entropy value of each indicator data, and calculate the real estate heat index of each city based on the obtained weight values; Step 5: Calculate the real estate heat index of all cities within the national administrative region at different times, and based on the obtained real estate heat index, obtain the logarithmic change rate of real estate heat of each city within a preset time period; Step 6: Based on the obtained logarithmic change rate of real estate heat in each city, calculate the cross-correlation matrix representing the real estate heat index of all cities within the national administrative region at different times; Step 7, performing random decomposition processing on the cross-correlation matrix to obtain decomposed matrix elements, and performing denoising processing on the decomposed matrix elements to obtain a denoised cross-correlation matrix; Step 8: Based on the obtained denoised cross-correlation matrix, construct a planar maximum filter graph of the denoised cross-correlation matrix, and use the planar maximum filter graph as a real estate heat correlation network representing the mutual correlation between all cities within the national administrative region; In step 9, the obtained real estate heat association network and the time series of the real estate heat index of each city within the national administrative region are used as input and input into the time graph convolutional network for processing, and the output result of the time graph convolutional network processing is used as the predicted value of the real estate market heat index of each city within the national administrative region.

2. The method for predicting real estate market heat index based on spatiotemporal correlation of multi-source data according to claim 1 is characterized in that: The abnormal data includes duplicate data and missing data.

3. The method for predicting real estate market heat index based on spatiotemporal correlation of multi-source data according to claim 2 is characterized in that: In step 3, the normalization process is performed to obtain the normalized multi-source indicator data set as follows: Among them, the normalized multi-source indicator data set is marked as x, x={x i,p (t)};x i,p (t) represents the i-th indicator data X of the p-th city i,p The normalized data value at time t, max(X i,p ) represents the i-th indicator data X of the p-th city i,p The maximum pre-processed data value in the pre-processed multi-source indicator data set X, min(X i,p ) represents the i-th indicator data X of the p-th city i,p The minimum preprocessed data value in the preprocessed multi-source indicator data set X.

4. The method for predicting real estate market heat index based on spatiotemporal correlation of multi-source data according to claim 3 is characterized in that: In step 3, the indicator weights of the indicator data at different times and the entropy values ​​of the indicator data are calculated as follows: Among them, U i,p (t) is the i-th indicator data X of the p-th city i,p The weight of the indicator at time t; is the normalized value x i,p (t) is the index data value after the self-increment processing, △ i is a positive number infinitely close to zero; Among them, e i,p is the entropy value of the i-th indicator data of the p-th city; Represents the i-th indicator data X of the p-th city i,p The self in the time period [t1,t m ] The corresponding indicator weight U i,p (t) the total number of 5. The method for predicting real estate market heat index based on spatiotemporal correlation of multi-source data according to claim 4 is characterized in that: In step 4, the coefficient of variation for each indicator data and the real estate heat index for each city are calculated as follows: g i,p =1-e i,p ; Among them, g i,p is the i-th indicator data X of the p-th city i,p The coefficient of variation, W i,p is the i-th indicator data X of the p-th city i,p The weight value of S p (t) is the real estate heat index of the p-th city at time t.

6. The method for predicting real estate market heat index based on spatiotemporal correlation of multi-source data according to claim 5 is characterized in that: In step 5, the logarithmic rate of change of real estate popularity in the city within the preset time period is calculated as follows: H p (t)=lnS p (t+△t)-lnS p (t);1≤p≤M; Among them, h p (t) represents the logarithmic change rate of real estate popularity in the p-th city within the preset time period △t, <H p (t) represents the average value of the sum of the real estate heat index of the p-th city within the preset time period △t, σ p is the standard deviation of the real estate heat index of the p-th city within the preset time period △t; the preset time period is a multiple of one day.

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