Economic index intelligent statistical analysis method and system

Abstract

The invention relates to the technical field of digital finance, and discloses an intelligent statistical analysis method and system for economic indicators, and the system comprises a module which improves the data processing efficiency, and achieves the automation of data collection, cleaning and storage through a distributed calculation frame and a data preprocessing module; the module is used for ensuring the accuracy and the consistency of the data, and ensuring the uniform format and the complete content of the multi-source data through a data standardization and cleaning algorithm; a module for realizing intelligent analysis and prediction, wherein a machine learning algorithm is adopted to perform intelligent analysis and prediction; a module for realizing real-time early warning and response and realizing real-time early warning through a threshold rule and an anomaly detection algorithm; according to the invention, full-process automation is realized through an intelligent means, and manual intervention is not needed from data acquisition to result display. The method not only provides the analysis of the current data, but also has the prediction capability of the economic trend, and fills the blank of the existing tool in the field.

Description

Technical Field

[0001] The present invention relates to the field of digital financial technology, and more specifically, to an intelligent statistical analysis method and system for economic indicators. Background Art

[0002] In the existing statistical analysis of economic indicators, traditional methods rely on manual statistics and analysis, with long data processing cycles, low efficiency, and prone to human errors, and cannot quickly adapt to the real-time changing economic environment. In addition, in the face of massive data, traditional analysis tools are difficult to extract valuable economic laws from them and lack the ability of intelligent processing. Therefore, the present invention is proposed to solve the following main technical problems:

[0003] 1. Low data processing efficiency: Traditional economic indicator statistics rely on manual data collation and analysis, which is time-consuming and prone to errors when faced with huge amounts of data.

[0004] Reason: The manual processing process is complicated, it is difficult to cope with massive data, and there is a lack of efficient data integration and processing mechanisms.

[0005] 2. Data accuracy and integrity issues: Data comes from multiple sources with inconsistent formats, structures and frequencies, making it difficult to ensure data accuracy and consistency.

[0006] Reason: Insufficient data standardization makes it difficult to integrate and analyze data from different sources, and there is data redundancy and loss.

[0007] 3. Lack of intelligent analysis and prediction: Traditional statistical methods can only process historical data and cannot effectively predict future economic trends.

[0008] Reason: The analysis method relies on simple statistical models, cannot handle complex nonlinear relationships, and lacks the support of intelligent analysis technologies such as machine learning.

[0009] 4. Slow response and inability to provide early warning: When economic data shows abnormalities, there is an inability to respond quickly or issue early warning signals, resulting in delayed decision-making.

[0010] Reason: Failure to implement real-time monitoring, and the speed of data processing and analysis cannot keep up with changes in the economic environment. Summary of the invention

[0011] The present invention provides an intelligent statistical analysis method and system for economic indicators, solves technical problems in related technologies, and proposes a systematic solution to the technical problems existing in traditional economic indicator statistics and analysis methods.

[0012] The present invention provides an economic indicator intelligent statistical analysis system, comprising:

[0013] A module to improve data processing efficiency, which uses a distributed computing framework and data preprocessing modules to automate data collection, cleaning, and storage;

[0014] A module that ensures data accuracy and consistency. It uses data standardization and cleaning algorithms to ensure that multi-source data has a unified format and complete content.

[0015] A module for intelligent analysis and prediction, which uses machine learning algorithms for intelligent analysis and prediction;

[0016] A module that implements real-time warning and response, and implements real-time warning through threshold rules and anomaly detection algorithms.

[0017] The present invention provides an intelligent statistical analysis system for economic indicators. The data acquisition module obtains raw data from multiple data sources through an API interface or a crawler script. Each data block is processed in parallel on distributed nodes to ensure the real-time and high efficiency of data processing.

[0018] During the cleaning process, data is deduplicated, missing values ​​are filled in according to rules, and unified formatting is performed for subsequent processing.

[0019] Automated data collection and cleaning improves the efficiency of data acquisition and processing, can quickly respond to massive amounts of data, and ensure the scalability and stability of the system.

[0020] The present invention provides an intelligent statistical analysis system for economic indicators, which uniformly converts the formats of economic indicator data from different sources, and uses a distributed parallel processing method for all data during the cleaning process to ensure the integrity and consistency of the data.

[0021] The system introduces a data verification mechanism to check and record abnormal situations in processing and automatically generate data processing reports for user review.

[0022] The present invention provides an intelligent statistical analysis system for economic indicators. The machine learning algorithm is LSTM. Standardized time series data is input. The LSTM model is trained according to past economic data, and weights and bias items are gradually adjusted. The trained model is used to predict future time steps and output future economic indicators.

[0023] The present invention provides an intelligent statistical analysis system for economic indicators. The machine learning algorithm is ARIMA. The original time series data is differentially processed to ensure data stability. The ACF and PACF graphs are used for analysis to determine the optimal autoregressive and sliding average orders p and q. The ARIMA model is fitted and trained to predict future economic trends.

[0024] The present invention provides an intelligent statistical analysis system for economic indicators. The machine learning algorithm is a K-means clustering algorithm. Multidimensional economic data is input, the number of clusters K is selected, and the K-means algorithm is iterated to output the center of each cluster and the cluster affiliation of each data point, so as to analyze the commonalities and differences of industries or regions.

[0025] The present invention provides an intelligent statistical analysis system for economic indicators, which sets monitored economic indicators and their corresponding thresholds. The system dynamically analyzes the data through a real-time monitoring module. When an indicator exceeds a preset threshold or an abnormal fluctuation occurs, an early warning message is immediately sent.

[0026] The present invention provides an intelligent statistical analysis system for economic indicators, which adopts distributed message queue technology to ensure the timeliness of early warning notification.

[0027] The present invention provides an economic indicator intelligent statistical analysis method, which performs the following steps through the aforementioned economic indicator intelligent statistical analysis system:

[0028] Use distributed computing framework and data preprocessing module to automate data collection, cleaning and storage;

[0029] Through data standardization and cleaning algorithms, ensure the uniform format and complete content of multi-source data;

[0030] Use machine learning algorithms for intelligent analysis and prediction;

[0031] Real-time warning is achieved through threshold rules and anomaly detection algorithms.

[0032] The beneficial effects of the present invention are:

[0033] Data processing efficiency has been significantly improved: the introduction of distributed processing architecture and automated data collection and cleaning technology has greatly improved the system's processing capabilities, enabling it to cope with massive amounts of economic data and reduce manual operation time.

[0034] Enhanced data accuracy and consistency: Standardization processing and outlier detection technology ensure data integrity and accuracy, reducing data redundancy and errors.

[0035] Intelligent analysis and prediction: Machine learning algorithms have improved the accuracy of forecasts of economic trends, especially in the modeling of complex nonlinear economic relationships, significantly improving the system's analytical capabilities.

[0036] Real-time early warning function: Through the intelligent early warning system, decision makers can receive timely warnings of economic fluctuations, improve the response speed to economic crises or market changes, and reduce decision-making risks.

[0037] Automation and intelligence: Compared with manual statistics, the present invention realizes full-process automation through intelligent means, and no human intervention is required from data collection to result display.

[0038] Predictive capabilities: The present invention not only provides analysis of current data, but also has the ability to predict economic trends, filling the gap in this area of ​​existing tools.

[0039] Intelligent early warning mechanism: Traditional economic data analysis tools only provide static data display. The present invention greatly improves the user's response speed to economic anomalies by introducing an intelligent early warning mechanism. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is a module schematic diagram of an economic indicator intelligent statistical analysis system of the present invention. DETAILED DESCRIPTION

[0041] The subject matter described herein will now be discussed with reference to example implementations. It should be understood that the discussion of these implementations is only to enable those skilled in the art to better understand and implement the subject matter described herein, and the functions and arrangements of the elements discussed may be changed without departing from the scope of protection of the present specification. Various examples may omit, replace, or add various processes or components as needed. In addition, the features described in some examples may also be combined in other examples.

[0042] At least one embodiment of the present invention discloses an economic indicator intelligent statistical analysis system. The following embodiments will specifically describe the modules that the economic indicator intelligent statistical analysis system may include.

[0043] In some embodiments of the present invention, a module for improving data processing efficiency is provided, including:

[0044] Solution: Use distributed computing frameworks (such as Hadoop and Spark) and data preprocessing modules to automate data collection, cleaning, and storage.

[0045] Technical means:

[0046] Distributed data processing framework: Through distributed computing systems such as Hadoop or Spark, data storage and processing tasks are assigned to multiple nodes for parallel processing, which greatly improves the processing speed and is especially suitable for massive economic data.

[0047] Automated data collection and cleaning: Use Python's Scrapy crawler framework to automatically crawl data, and use data analysis tools such as Pandas for cleaning, including missing value processing, outlier detection and standardization.

[0048] Implementation process:

[0049] The data acquisition module obtains raw data from multiple data sources through API interfaces or crawler scripts. Each data block is processed in parallel on distributed nodes to ensure real-time and efficient data processing.

[0050] During the cleaning process, data is deduplicated, missing values ​​are filled in according to rules, and unified formatting is performed for subsequent processing.

[0051] Automated data collection and cleaning improves the efficiency of data acquisition and processing, can quickly respond to massive amounts of data, and ensure the scalability and stability of the system.

[0052] In some embodiments of the present invention, a module for ensuring the accuracy and consistency of data is provided, including:

[0053] Solution: Ensure the uniform format and complete content of multi-source data through data standardization and cleaning algorithms.

[0054] Technical means:

[0055] Data standardization: Use Min-Max Scaling or Z-score Standardization to standardize economic data from different sources and unify them into the same scale range.

[0056] Outlier processing: Identify and remove outliers through box plot analysis and Z-score method. For continuous time series data, interpolation or KNN interpolation method is used to fill missing values.

[0057] Implementation process:

[0058] The economic indicator data from different sources are uniformly formatted and all data are processed in a distributed parallel manner during the cleaning process to ensure data integrity and consistency.

[0059] The system introduces a data verification mechanism to check and record abnormal situations in processing and automatically generate data processing reports for user review.

[0060] Data accuracy and consistency have been greatly improved. The system can efficiently process multi-source data, achieve seamless integration, and avoid errors and redundancy problems in traditional manual operations.

[0061] In some embodiments of the present invention, a module for implementing intelligent analysis and prediction is provided, including:

[0062] Solution: Use machine learning algorithms (such as LSTM, ARIMA, etc.) for intelligent analysis and prediction to solve the problem of modeling nonlinear economic relationships.

[0063] LSTM (Long Short-Term Memory)

[0064] **LSTM (Long Short-Term Memory)** is a recurrent neural network (RNN) model suitable for processing time series data. Different from traditional RNN, LSTM can remember long-term dependency information by introducing a "memory unit" mechanism, effectively avoiding the gradient vanishing problem.

[0065] The core of LSTM lies in its three gate units: Forget Gate, Input Gate, and Output Gate, which control the flow of information in each time step (t).

[0066] The tth output f of the forget gate t for:

[0067] f t =σ(W f ·[h t-1 ,x t ]+b f );

[0068] Among them, W f is the weight matrix of the forget gate, b f is the bias term of the forget gate, [ h t-1 ,x t] is the t-1th hidden state h t-1 and x t splicing, σ is an S-type function; x t Represents the tth sequence unit of the sequence data;

[0069] The t-th output i of the input gate t and the state of the tth candidate cell The calculation method is:

[0070] i t =σ(W i ·[h t-1 ,x t ]+b i );

[0071]

[0072] Among them, W i is the weight matrix of the input gate, b i is the bias term of the input gate, tanh is the hyperbolic tangent activation function, and W C is the weight matrix for calculating the candidate cell state, b C is the bias term of the candidate cell state;

[0073] The tth cell state C t is the state C of the t-1th cell t-1 and i t Weighted synthesis, the calculation method is:

[0074]

[0075] Among them, * represents element-by-element multiplication operation;

[0076] The tth activation value o of the output gate t With the tth hidden state h t The calculation method is:

[0077] o t =σ(W o ·[h t-1 ,x t ]+b o );

[0078] h t =o t *tanh(C t );

[0079] Among them, W o is the weight matrix of the output gate, b o is the bias term of the output gate.

[0080] By repeatedly updating and passing information, LSTM is able to learn long-term dependencies in time series data, which can be used to predict future economic indicators.

[0081] Implementation process:

[0082] Input standardized time series data (such as GDP, CPI).

[0083] The LSTM model trains the model based on past economic data and gradually adjusts the weights and bias terms.

[0084] Use the trained model to predict future time steps and output future economic indicators.

[0085] ARIMA (Autoregressive Integrated Moving Average Model)

[0086] The ARIMA model is a classic statistical method, commonly used for linear time series forecasting. It combines autoregression (AR), difference (I) and moving average (MA) to process and forecast economic time series data.

[0087] ARIMA formula and explanation:

[0088] Autoregressive part (AR):

[0089] Assume that the current value is obtained by linear combination of the values ​​of the previous periods. The mathematical formula is:

[0090] X t =c+φ1X t-1 +φ2X t-2 +...+φ p X t-p +∈ t

[0091] X t is the value of the time series at time t.

[0092] φ1, φ2, ..., φ p is the autoregressive coefficient, which represents the weight of the previous period values.

[0093] ∈ t is the error term.

[0094] Differential part (I):

[0095] The trend is eliminated by differencing to make the time series stationary.

[0096] Y t =X t -X t-1

[0097] Y t Represents a time series after a difference.

[0098] For data that requires higher-order differences, the number of differences d can be set to 1 or more.

[0099] Moving average part (MA):

[0100] The current value is also affected by the error in previous forecasts.

[0101] X t =μ+∈ t +θ1∈ t-1 +θ2∈ t-2 +...+θ q ∈ t-q

[0102] θ1, θ2, ..., θ q is the moving average coefficient, which represents the weight of past forecast errors.

[0103] The combined formula of the ARIMA model is:

[0104] ARIMA(p, d, q)

[0105] p: Autoregressive order (AR part).

[0106] d: number of differences (part I).

[0107] q: sliding average order (MA part).

[0108] Implementation process:

[0109] Perform difference processing on the original time series data to ensure the data is stable.

[0110] Use ACF and PACF plot analysis to determine the optimal autoregressive and moving average orders p and q.

[0111] Through fitting and training of ARIMA model, future economic trends can be predicted.

[0112] K-means clustering algorithm

[0113] K-means is an unsupervised learning algorithm used to divide data points into K clusters so that data points in the same cluster are similar to each other. It is suitable for cluster analysis of economies or industries.

[0114] K-means formula and explanation:

[0115] K-means minimizes the distance between the data points in a cluster and the cluster center by minimizing the squared error within the cluster.

[0116] Initial cluster centers:

[0117] Randomly select K data points as the initial cluster centers.

[0118] Cluster allocation:

[0119] Assign each data point to the nearest cluster center C_k. The formula is:

[0120] d(x i , C k )=min||x i -C k ||

[0121] x i is a data point, C k is the cluster center, d(x i , C k ) represents the distance between the data point and the cluster center.

[0122] Update cluster center:

[0123] Recalculate the center position of each cluster using the formula:

[0124]

[0125] S k is the set of data points belonging to cluster k, |Sk |.

[0126] Iteration:

[0127] The cluster assignment and cluster center update process is repeated until the cluster center no longer changes or the maximum number of iterations is reached.

[0128] Implementation process:

[0129] Input multi-dimensional economic data, such as GDP, CPI, employment rate, etc. of different regions.

[0130] The number of clusters K is selected and iterated through the K-means algorithm.

[0131] Output the center of each cluster and the cluster affiliation of each data point to help users analyze the commonalities and differences in industries or regions.

[0132] In some embodiments of the present invention, a module for implementing real-time early warning and response is provided, including:

[0133] Solution: Build an intelligent early warning system to achieve real-time warning through threshold rules and anomaly detection algorithms.

[0134] Technical means:

[0135] Threshold monitoring: Set the threshold range for key economic indicators. When the indicator exceeds the range, the system automatically issues an alarm.

[0136] Anomaly detection algorithm: Use unsupervised learning algorithms (such as Isolation Forest) or statistical anomaly detection methods (such as Z-score method) to detect abnormal changes in economic data and trigger early warnings.

[0137] Implementation process:

[0138] Users can set the economic indicators to be monitored and their corresponding thresholds. The system dynamically analyzes the data through the real-time monitoring module, and immediately sends an early warning message when an indicator exceeds the preset threshold or an abnormal fluctuation occurs.

[0139] The system uses distributed message queue technology (such as Kafka) to ensure the timeliness of early warning notifications.

[0140] The above describes an embodiment of the present invention, but this embodiment is not limited to the above-mentioned specific implementation mode. The above-mentioned specific implementation mode is merely illustrative and not restrictive. Under the guidance of this embodiment, ordinary technicians in this field can also make more forms of equivalent embodiments, all of which are within the protection of this embodiment.

Claims

1. An intelligent statistical analysis system for economic indicators, characterized in that: include: A module to improve data processing efficiency, which uses a distributed computing framework and data preprocessing modules to automate data collection, cleaning, and storage; A module that ensures data accuracy and consistency. It uses data standardization and cleaning algorithms to ensure that multi-source data has a unified format and complete content. A module for intelligent analysis and prediction, which uses machine learning algorithms for intelligent analysis and prediction; A module that implements real-time warning and response, and realizes real-time warning through threshold rules and anomaly detection algorithms.

2. The economic indicator intelligent statistical analysis system according to claim 1, characterized in that: The data acquisition module obtains raw data from multiple data sources through API interfaces or crawler scripts; each data block is processed in parallel on distributed nodes; During the cleaning process, data is deduplicated, missing values ​​are filled in according to rules, and unified formatting is performed for subsequent processing; Automated data collection and cleaning improves the efficiency of data acquisition and processing, can quickly respond to massive amounts of data, and ensure the scalability and stability of the system.

3. The economic indicator intelligent statistical analysis system according to claim 1, characterized in that: The economic indicator data from different sources are uniformly converted into formats, and all data are processed in a distributed parallel manner during the cleaning process to ensure data integrity and consistency; The system introduces a data verification mechanism to check and record abnormal situations in processing and automatically generate data processing reports for user review.

4. The economic indicator intelligent statistical analysis system according to claim 1, characterized in that: The machine learning algorithm is LSTM, which inputs standardized time series data. The LSTM model is trained based on past economic data, gradually adjusts weights and bias items, uses the trained model to predict future time steps, and outputs future economic indicators.

5. The economic indicator intelligent statistical analysis system according to claim 1, characterized in that: The machine learning algorithm is ARIMA, which performs difference processing on the original time series data to ensure data stability; uses ACF and PACF graph analysis to determine the optimal autoregression and sliding average orders p and q; and fits and trains the ARIMA model to predict future economic trends.

6. The economic indicator intelligent statistical analysis system according to claim 1, characterized in that: The machine learning algorithm is the K-means clustering algorithm, which inputs multi-dimensional economic data, selects the number of clusters K, and iterates through the K-means algorithm to output the center of each cluster and the cluster affiliation of each data point to analyze the commonalities and differences of industries or regions.

7. The economic indicator intelligent statistical analysis system according to claim 1, characterized in that: Set the monitored economic indicators and their corresponding thresholds. The system will dynamically analyze the data through the real-time monitoring module. When an indicator exceeds the preset threshold or experiences abnormal fluctuations, an early warning message will be sent immediately.

8. The economic indicator intelligent statistical analysis system according to claim 7, characterized in that: Distributed message queue technology is used to ensure the timeliness of early warning notifications.

9. An intelligent statistical analysis method for economic indicators, characterized in that: The following steps are performed by the economic indicator intelligent statistical analysis system as described in any one of claims 1 to 8: Use distributed computing framework and data preprocessing module to automate data collection, cleaning and storage; Through data standardization and cleaning algorithms, ensure the uniform format and complete content of multi-source data; Use machine learning algorithms for intelligent analysis and prediction; Real-time warning is achieved through threshold rules and anomaly detection algorithms.

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