CN115577093A - AI analysis method and system of financial information - Google Patents
AI analysis method and system of financial information Download PDFInfo
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- CN115577093A CN115577093A CN202210588569.9A CN202210588569A CN115577093A CN 115577093 A CN115577093 A CN 115577093A CN 202210588569 A CN202210588569 A CN 202210588569A CN 115577093 A CN115577093 A CN 115577093A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/33—Querying
- G06F16/335—Filtering based on additional data, e.g. user or group profiles
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9535—Search customisation based on user profiles and personalisation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/958—Organisation or management of web site content, e.g. publishing, maintaining pages or automatic linking
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
- G06F40/20—Natural language analysis
- G06F40/279—Recognition of textual entities
- G06F40/289—Phrasal analysis, e.g. finite state techniques or chunking
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- G—PHYSICS
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
Abstract
The application provides an AI analysis method system of financial information, the method includes: the terminal searches policy information within a specific period of time before a specific time point at the specific time point; the terminal carries out word segmentation processing on the policy information to obtain a plurality of keywords of the policy information, inputs the keywords into an AI model for type recognition to determine an industry keyword and a policy department keyword in the keywords; the terminal extracts a first level corresponding to the keyword of the policy department, inquires historical policy information identical to the first level, inquires m pieces of historical policy information identical to the keyword of the industry from the historical policy information, extracts a first trend after the release date of the historical policy information corresponding to the first historical trend, determines the first trend as an analysis result corresponding to the financial information, and pushes the analysis result to a user. The technical scheme provided by the application has the advantage of high user experience.
Description
Technical Field
The invention relates to the field of finance and Internet, in particular to an AI analysis method and system for financial information.
Background
Financial information refers to signals, instructions, data, situations, messages generated in the process of organizing and managing currency circulation, various financial security transactions, credit activities, and fund settlement, and the contents thereof include government financial information, exchange rate information, stock market information, customer credit information, and the like.
The existing financial information analysis is manually analyzed, so that the financial information cannot be automatically analyzed, and the timeliness of financial analysis is further influenced.
Disclosure of Invention
The embodiment of the invention provides a financial information AI analysis method and system, which can analyze financial information in an AI mode, improve the timeliness of financial analysis and improve the user experience.
In a first aspect, an embodiment of the present invention provides a method for AI analysis of financial information, where the method includes the following steps:
the terminal searches policy information within a specific period of time before a specific time point at the specific time point;
the terminal carries out word segmentation processing on the policy information to obtain a plurality of keywords of the policy information, and inputs the keywords into an AI model for type recognition to determine the industry keywords and the policy department keywords in the keywords;
the terminal extracts a first level corresponding to the keyword of the policy department, inquires historical policy information identical to the first level, inquires m pieces of historical policy information identical to the keyword of the industry from the historical policy information, acquires m pieces of historical trends corresponding to the m pieces of historical policy information, searches a first historical trend most similar to the current trend from the m pieces of historical trends, extracts a first trend behind the release date of the historical policy information corresponding to the first historical trend, determines the first trend as an analysis result corresponding to the financial information, and pushes the analysis result to a user.
Optionally, the industry keywords include: industry name or industry concept word.
Optionally, the obtaining m historical trends corresponding to m pieces of historical policy information specifically includes:
acquiring m issuing times of m pieces of historical policy information, and extracting m historical trends of industries corresponding to the m pieces of historical policy information in a set time interval, wherein the set time interval is a time interval with set time duration, and the central point of the set time interval is the issuing time of the m pieces of historical policy information.
Optionally, the searching for the first historical trend most similar to the current trend from the m historical trends specifically includes:
acquiring the number x of days of the current trend, extracting x price k lines of the previous x days, extracting m segmented trends before m release times from m historical trends, and inquiring the historical trend corresponding to the segmented trend most approximate to the x price k lines from the m segmented trends to determine the historical trend as a first historical trend.
In a second aspect, there is provided an AI analysis system for financial information, the system comprising:
a search unit for searching for policy information within a specific period before a specific time point at the specific time point;
the processing unit is used for performing word segmentation processing on the policy information to obtain a plurality of keywords of the policy information, inputting the keywords into an AI model for type recognition to determine an industry keyword and a policy department keyword in the plurality of keywords; extracting a first level corresponding to the keyword of the policy department, inquiring historical policy information identical to the first level, inquiring m pieces of historical policy information identical to the keyword of the industry from the historical policy information, acquiring m pieces of historical trends corresponding to the m pieces of historical policy information, searching a first historical trend most similar to the current trend from the m pieces of historical trends, extracting a first trend after the release date of the historical policy information corresponding to the first historical trend, determining the first trend as an analysis result corresponding to the financial information, and pushing the analysis result to a user.
Optionally, the industry keywords include: industry name or industry concept word.
Optionally, the processing unit is specifically configured to obtain m issuance times of the m pieces of historical policy information, extract m historical trends of industries corresponding to the m pieces of historical policy information in a set time interval, where the set time interval is a time interval with a set duration, and a central point of the set time interval is the issuance time of the m pieces of historical policy information.
Optionally, the processing unit is specifically configured to obtain the number x of days of the current trend, extract x price k lines of the previous x days, extract m segment trends before m release times from the m historical trends, query a historical trend corresponding to a segment trend most similar to the x price k lines from the m segment trends, and determine that the historical trend is the first historical trend.
In a third aspect, a computer-readable storage medium is provided that stores a program for electronic data exchange, wherein the program causes a terminal to execute the method provided by the first aspect.
The embodiment of the invention has the following beneficial effects:
it can be seen that the terminal searches for policy information within a specific time period before a specific time point at the specific time point according to the technical scheme provided by the application; the terminal carries out word segmentation processing on the policy information to obtain a plurality of keywords of the policy information, inputs the keywords into an AI model for type recognition to determine an industry keyword and a policy department keyword in the keywords; the terminal extracts a first level corresponding to the keyword of the policy department, inquires historical policy information identical to the first level, inquires m pieces of historical policy information identical to the keyword of the industry from the historical policy information, acquires m pieces of historical trends corresponding to the m pieces of historical policy information, searches a first historical trend most similar to the current trend from the m pieces of historical trends, extracts a first trend behind the release date of the historical policy information corresponding to the first historical trend, determines the first trend as an analysis result corresponding to the financial information, and pushes the analysis result to a user. According to the technical scheme, the corresponding recommendation trend is obtained through the classification processing of the policy information, and then corresponding recommendation is provided for the user, so that the user experience is improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a schematic diagram of a terminal
FIG. 2 is a flow chart illustrating a method for AI analysis of financial information;
fig. 3 is a schematic structural diagram of an AI analysis system for financial information.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The terms "first," "second," "third," and "fourth," etc. in the description and claims of the invention and in the accompanying drawings are used for distinguishing between different objects and not for describing a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements but may alternatively include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Reference herein to "an embodiment" means that a particular feature, result, or characteristic described in connection with the embodiment can be included in at least one embodiment of the invention. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
Referring to fig. 1, fig. 1 provides a terminal, which may be a terminal of an IOS system, an android system, or a terminal of another system, such as a hong meng system, etc., where the present application does not limit the specific system, and as shown in fig. 1, the terminal device may specifically include: the processor, the memory, the display screen, the communication circuit and the audio component (optional), and the above components may be connected through a bus, and may also be connected in other manners, and the present application does not limit the specific manner of the above connection.
Referring to fig. 2, fig. 2 provides an AI analysis method for financial information, which is shown in fig. 2 and can be implemented in a terminal, where the terminal is connected to a network device in a wireless manner, and the wireless manner may be a wireless communication system.
Step S201, the terminal searches policy information in a specific time period before a specific time point at the specific time point;
the specific time point may be a specific time period of 8 am or 7 am, and the specific time period may be 24 hours, 48 hours, 1 week, and the like.
Step S202, the terminal carries out word segmentation processing on the policy information to obtain a plurality of keywords of the policy information, and inputs the keywords into an AI model to carry out type recognition to determine an industry keyword and a policy department keyword in the keywords;
for example, the word segmentation processing manner may be an existing word segmentation processing manner, and the word segmentation processing manner is not limited herein.
By way of example, the aforementioned industry keywords include, but are not limited to: industry names such as wind, coal, electricity, etc., although industry keywords may also be industry concept words, including but not limited to: the meta universe, east county, west, etc.
For example, the AI model may specifically include: a classifier or neural network model, etc.
Step S203, the terminal extracts a first level corresponding to the keyword of the policy department, inquires historical policy information the same as the first level, inquires m pieces of historical policy information the same as the keyword of the industry from the historical policy information, obtains m pieces of historical trends corresponding to the m pieces of historical policy information, searches a first historical trend most similar to the current trend from the m pieces of historical trends, extracts a first trend after the release date of the historical policy information corresponding to the first historical trend, determines the first trend as an analysis result corresponding to the financial information, and pushes the analysis result to a user.
For example, the above levels may specifically include: and (4) administrative level.
For example, the obtaining m historical trends corresponding to m pieces of historical policy information may specifically include:
acquiring m issuing times of m pieces of historical policy information, and extracting m historical trends of industries corresponding to the m pieces of historical policy information in a set time interval, wherein the set time interval is a time interval with set time duration, and the central point of the set time interval is the issuing time of the m pieces of historical policy information.
The set time interval may be 1 month or 2 months, and the m issue times are based on days, for example, 5 months and 18 days in 2020.
For example, the searching for the first historical trend most similar to the current trend from the m historical trends may specifically include:
acquiring the number x of days of the current trend, extracting x price k lines of the previous x days, extracting m segmented trends before m release times from m historical trends, and inquiring the historical trend corresponding to the segmented trend most approximate to the x price k lines from the m segmented trends to determine the historical trend as a first historical trend.
For example, the extracting the first trend after the release date of the historical policy information corresponding to the first historical trend may specifically include:
and determining the fluctuation situation and the fluctuation amplitude total amount of the first historical trend after the release time of the first historical trend, and determining the fluctuation situation and the fluctuation amplitude total amount as the information contained in the first trend.
Referring to fig. 3, fig. 3 is a schematic structural diagram of an AI analysis system for financial information, the system including:
a searching unit 301 for searching for policy information within a specific period before a specific time point at the specific time point;
the processing unit 302 is configured to perform word segmentation on the policy information to obtain a plurality of keywords of the policy information, and input the plurality of keywords into an AI model to perform type identification to determine an industry keyword and a policy department keyword in the plurality of keywords; extracting a first level corresponding to the keyword of the policy department, inquiring historical policy information identical to the first level, inquiring m pieces of historical policy information identical to the keyword of the industry from the historical policy information, acquiring m pieces of historical trends corresponding to the m pieces of historical policy information, searching a first historical trend most similar to the current trend from the m pieces of historical trends, extracting a first trend after the release date of the historical policy information corresponding to the first historical trend, determining the first trend as an analysis result corresponding to the financial information, and pushing the analysis result to a user.
According to the technical scheme, the terminal searches policy information in a specific time period before a specific time point at the specific time point; the terminal carries out word segmentation processing on the policy information to obtain a plurality of keywords of the policy information, inputs the keywords into an AI model for type recognition to determine an industry keyword and a policy department keyword in the keywords; the terminal extracts a first level corresponding to the keyword of the policy department, inquires historical policy information identical to the first level, inquires m pieces of historical policy information identical to the keyword of the industry from the historical policy information, acquires m pieces of historical trends corresponding to the m pieces of historical policy information, searches a first historical trend most similar to the current trend from the m pieces of historical trends, extracts a first trend behind the release date of the historical policy information corresponding to the first historical trend, determines the first trend as an analysis result corresponding to the financial information, and pushes the analysis result to a user. According to the technical scheme, the corresponding recommendation trend is obtained through the classification processing of the policy information, and then corresponding recommendation is provided for the user, and the user experience is improved.
Optionally, the industry keywords include: industry name or industry concept word.
Optionally, the processing unit is specifically configured to obtain m issuance times of the m pieces of historical policy information, extract m historical trends of industries corresponding to the m pieces of historical policy information in a set time interval, where the set time interval is a time interval with a set duration, and a central point of the set time interval is the issuance time of the m pieces of historical policy information.
Optionally, the processing unit is specifically configured to obtain the number x of days of the current trend, extract x price k lines of the previous x days, extract m segment trends before m release times from the m historical trends, query a historical trend corresponding to a segment trend most similar to the x price k lines from the m segment trends, and determine that the historical trend is the first historical trend.
An embodiment of the present invention also provides a computer storage medium, wherein the computer storage medium stores a computer program for electronic data exchange, the computer program causing a computer to execute a part or all of the steps of any one of the AI analysis methods of financial information as described in the above method embodiments.
Embodiments of the present invention also provide a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, the computer program being operable to cause a computer to perform part or all of the steps of any one of the methods for AI analysis of financial information as described in the above method embodiments.
It should be noted that, for simplicity of description, the above-mentioned method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present invention is not limited by the order of acts, as some steps may be performed in other orders or concurrently according to the present invention. Further, those skilled in the art should also appreciate that the embodiments described in the specification are exemplary embodiments and that the acts and modules illustrated are not necessarily required to practice the invention.
In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus may be implemented in other manners. For example, the above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one type of division of logical functions, and there may be other divisions when actually implementing, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of some interfaces, devices or units, and may be an electric or other form.
Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by associated hardware instructed by a program, which may be stored in a computer-readable memory, which may include: flash Memory disks, read-Only memories (ROMs), random Access Memories (RAMs), magnetic or optical disks, and the like.
The above embodiments of the present invention are described in detail, and the principle and the implementation of the present invention are explained by applying specific embodiments, and the description of the above embodiments is only used to help understanding the method of the present invention and the core idea thereof; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
Claims (9)
1. An AI analysis method of financial information, the method comprising the steps of:
the terminal searches policy information within a specific period of time before a specific time point at the specific time point;
the terminal carries out word segmentation processing on the policy information to obtain a plurality of keywords of the policy information, inputs the keywords into an AI model for type recognition to determine an industry keyword and a policy department keyword in the keywords;
the terminal extracts a first level corresponding to the keyword of the policy department, inquires historical policy information identical to the first level, inquires m pieces of historical policy information identical to the keyword of the industry from the historical policy information, acquires m pieces of historical trends corresponding to the m pieces of historical policy information, searches a first historical trend most similar to the current trend from the m pieces of historical trends, extracts a first trend after the release date of the historical policy information corresponding to the first historical trend, determines the first trend as an analysis result corresponding to the financial information, and pushes the analysis result to a user.
2. The method of claim 1, wherein the industry keywords comprise: industry name or industry concept word.
3. The method according to claim 1, wherein the obtaining m historical trends corresponding to m pieces of historical policy information specifically includes:
acquiring m issuing times of m pieces of historical policy information, and extracting m historical trends of industries corresponding to the m pieces of historical policy information in a set time interval, wherein the set time interval is a time interval with set time duration, and the central point of the set time interval is the issuing time of the m pieces of historical policy information.
4. The method according to claim 1, wherein the searching for the first historical trend that is most similar to the current trend from the m historical trends specifically comprises:
acquiring the number x of days of the current trend, extracting x price k lines of the previous x days, extracting m segmented trends before m release times from m historical trends, and inquiring the historical trend corresponding to the segmented trend most approximate to the x price k lines from the m segmented trends to determine the historical trend as a first historical trend.
5. An AI analysis system of financial information, the system comprising:
a search unit for searching for policy information within a specific period before a specific time point at the specific time point;
the processing unit is used for performing word segmentation processing on the policy information to obtain a plurality of keywords of the policy information, inputting the keywords into an AI model for type recognition to determine an industry keyword and a policy department keyword in the keywords; extracting a first level corresponding to the keyword of the policy department, inquiring historical policy information identical to the first level, inquiring m pieces of historical policy information identical to the keyword of the industry from the historical policy information, acquiring m pieces of historical trends corresponding to the m pieces of historical policy information, searching a first historical trend most similar to the current trend from the m pieces of historical trends, extracting a first trend after the release date of the historical policy information corresponding to the first historical trend, determining the first trend as an analysis result corresponding to the financial information, and pushing the analysis result to a user.
6. The system of claim 5, wherein the industry keywords comprise: industry name or industry concept word.
7. The system of claim 5,
the processing unit is specifically configured to acquire m issuance times of m pieces of historical policy information, extract m historical trends of industries corresponding to the m pieces of historical policy information in a set time interval, where the set time interval is a time interval with a set duration, and a central point of the set time interval is the issuance time of the m pieces of historical policy information.
8. The system of claim 5,
the processing unit is specifically configured to acquire the number x of days of the current trend, extract x price k lines of the previous x days, extract m segment trends before m release times from the m historical trends, and query the historical trend corresponding to the segment trend most similar to the x price k lines from the m segment trends to determine that the historical trend is the first historical trend.
9. A computer-readable storage medium storing a program for electronic data exchange, wherein the program causes a terminal to perform the method as provided in any one of claims 1-4.
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Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030135445A1 (en) * | 2001-01-22 | 2003-07-17 | Herz Frederick S.M. | Stock market prediction using natural language processing |
| CN104317910A (en) * | 2014-10-27 | 2015-01-28 | 国家电网公司 | Data processing method and device |
| CN105022825A (en) * | 2015-07-22 | 2015-11-04 | 中国人民解放军国防科学技术大学 | Financial variety price prediction method capable of combining financial news mining and financial historical data |
| CN105912564A (en) * | 2016-03-23 | 2016-08-31 | 广州新博庭网络信息科技股份有限公司 | Feature business object mining method, feature business object display method, feature business object mining device and feature business object display device |
| CN106407272A (en) * | 2016-08-25 | 2017-02-15 | 广州新博庭网络信息科技股份有限公司 | Service statistical line display method and device |
| CN106557979A (en) * | 2016-11-23 | 2017-04-05 | 广州盛星元信息科技有限公司 | Based on GPU acceleration and the finance data analysis method and platform of paralleling genetic algorithm |
| CN107230149A (en) * | 2017-07-02 | 2017-10-03 | 洪志令 | A kind of Stock Trading point calculating method and its determination strategy on dealing opportunity |
| CN107481143A (en) * | 2017-07-28 | 2017-12-15 | 武汉楚鼎信息技术有限公司 | A kind of intelligent stock commending system and implementation method |
| CN108446790A (en) * | 2018-02-10 | 2018-08-24 | 灯塔财经信息有限公司 | A kind of similar K lines prediction technique and device |
| CN108647828A (en) * | 2018-05-15 | 2018-10-12 | 中山大学 | A kind of Prediction of Stock Index method of combination news corpus and stock market's transaction data |
| CN110059267A (en) * | 2019-03-01 | 2019-07-26 | 平安科技（深圳）有限公司 | Methods of exhibiting, device, computer equipment and the storage medium of data analysis result |
| CN110489631A (en) * | 2019-07-10 | 2019-11-22 | 平安科技（深圳）有限公司 | Stock market development method, apparatus, computer equipment and storage medium |
| US10963799B1 (en) * | 2016-05-05 | 2021-03-30 | Wells Fargo Bank, N.A. | Predictive data analysis of stocks |
| CN113435197A (en) * | 2021-06-25 | 2021-09-24 | 平安国际智慧城市科技股份有限公司 | Data pushing method and device, pushing server and storage medium |
| CN114461867A (en) * | 2022-04-12 | 2022-05-10 | 深圳格隆汇信息科技有限公司 | Financial big data analysis processing method and system |
-
2022
- 2022-05-26 CN CN202210588569.9A patent/CN115577093B/en active Active
Patent Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030135445A1 (en) * | 2001-01-22 | 2003-07-17 | Herz Frederick S.M. | Stock market prediction using natural language processing |
| CN104317910A (en) * | 2014-10-27 | 2015-01-28 | 国家电网公司 | Data processing method and device |
| CN105022825A (en) * | 2015-07-22 | 2015-11-04 | 中国人民解放军国防科学技术大学 | Financial variety price prediction method capable of combining financial news mining and financial historical data |
| CN105912564A (en) * | 2016-03-23 | 2016-08-31 | 广州新博庭网络信息科技股份有限公司 | Feature business object mining method, feature business object display method, feature business object mining device and feature business object display device |
| US10963799B1 (en) * | 2016-05-05 | 2021-03-30 | Wells Fargo Bank, N.A. | Predictive data analysis of stocks |
| CN106407272A (en) * | 2016-08-25 | 2017-02-15 | 广州新博庭网络信息科技股份有限公司 | Service statistical line display method and device |
| CN106557979A (en) * | 2016-11-23 | 2017-04-05 | 广州盛星元信息科技有限公司 | Based on GPU acceleration and the finance data analysis method and platform of paralleling genetic algorithm |
| CN107230149A (en) * | 2017-07-02 | 2017-10-03 | 洪志令 | A kind of Stock Trading point calculating method and its determination strategy on dealing opportunity |
| CN107481143A (en) * | 2017-07-28 | 2017-12-15 | 武汉楚鼎信息技术有限公司 | A kind of intelligent stock commending system and implementation method |
| CN108446790A (en) * | 2018-02-10 | 2018-08-24 | 灯塔财经信息有限公司 | A kind of similar K lines prediction technique and device |
| CN108647828A (en) * | 2018-05-15 | 2018-10-12 | 中山大学 | A kind of Prediction of Stock Index method of combination news corpus and stock market's transaction data |
| CN110059267A (en) * | 2019-03-01 | 2019-07-26 | 平安科技（深圳）有限公司 | Methods of exhibiting, device, computer equipment and the storage medium of data analysis result |
| CN110489631A (en) * | 2019-07-10 | 2019-11-22 | 平安科技（深圳）有限公司 | Stock market development method, apparatus, computer equipment and storage medium |
| CN113435197A (en) * | 2021-06-25 | 2021-09-24 | 平安国际智慧城市科技股份有限公司 | Data pushing method and device, pushing server and storage medium |
| CN114461867A (en) * | 2022-04-12 | 2022-05-10 | 深圳格隆汇信息科技有限公司 | Financial big data analysis processing method and system |
Non-Patent Citations (2)
| Title |
|---|
| WEN M等: "stock market trend prediction using high order information of time series", IEEE ACCESS * |
| 童芳;兰凤崇;陈吉清;: "新能源汽车发展影响因素分析及保有量预测", 科技管理研究 * |
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