CN106203668B

CN106203668B - System for carrying out predictive analysis and display on information index

Info

Publication number: CN106203668B
Application number: CN201510228706.8A
Authority: CN
Inventors: 谭航
Original assignee: Beijing Jixun Zhicheng Network Technology Co ltd
Current assignee: Beijing Jixun Zhicheng Network Technology Co ltd
Priority date: 2015-05-07
Filing date: 2015-05-07
Publication date: 2021-04-20
Anticipated expiration: 2035-05-07
Also published as: CN106203668A

Abstract

The invention discloses a system for carrying out predictive analysis and display on an information index, which comprises: the reference index unit is used for displaying and determining a reference index; the historical data processing unit is used for calculating, searching, displaying and uploading the historical data of the information index, calculating the third-party information index by combining the data of the client and then issuing the third-party information index; the prediction and comparison unit is used for predicting the future information index and comparing and displaying a plurality of prediction results; the data combination unit is used for forming the historical data of the information index or the information index set, the quantitative prediction data of the client, the qualitative analysis of the client or the client team and the final action countermeasure into iterative merged display of information history, prediction and action countermeasures, providing the function of searching the past iterative merged display, and effectively performing future prediction and historical analysis on the information index; and a prediction history analysis unit.

Description

System for carrying out predictive analysis and display on information index

Technical Field

The invention relates to the field of information index prediction, in particular to a system for performing prediction analysis and display on an information index.

Background

Currently, for the information index concerned by enterprises, a few large clients store the raw data of the information index and perform future prediction and historical analysis through enterprise information integration software such as DSS, MIS or BI, most small and micro enterprises or individuals using data reports or documents.

Specifically, the customer manually enters the information index issued by the third-party information platform into enterprise information integration software of an enterprise where the customer is located or a personal data report or document. The historical data is automatically calculated by the calculation function of the software, the report or the document and the analysis method designed by the client. And guessing the numerical value of a period of time or a time point in the future by combining historical data, and continuously inputting the software, the report or the document. The guessed data of a certain period in the past is compared with the actual information index, and the deviation of the current forecast of the client is judged.

The problems of the future prediction and history analysis method are as follows: (1) the client cannot publish the own information index to a third-party platform and form a reference index, and meanwhile, the client or other business-related multiple clients can predict the future in a mutually visible or invisible mode. (2) For the information indexes issued by the third-party platform or the client, the client can not freely select different storage environments such as a client server, the third-party platform or a cloud according to needs, and on the premise of data compatibility, the client can calculate, search, display and upload in an option mode of combining with or not combining with the client's own future prediction data. (3) The future prediction data and graphs satisfied by the customer can not be described by only sliding the finger without typing any number. And the online comparison display of multiple clients cannot be realized. (4) The customers have to update or record own future prediction in a traditional mode every day, iterative information prediction merging display of information index historical data and customer prediction data which are updated automatically according to the day at the same time cannot be realized, multi-person operation of the merging display cannot be realized, and past iterative merging display cannot be induced and searched for iterative merging display of the information history, prediction and action countermeasures. (5) The level of future predictions for the same benchmark index by different customers cannot be quantitatively described, and the level cannot be automatically notified to interested third-party customers.

In summary, there is dissatisfaction of customers with the current state of the art future prediction and history analysis software or methods. If the experience results of future prediction and historical analysis which are particularly satisfactory to the client cannot be provided, the client cannot form the future predicted behavior into the mass behavior, and the future predicted behavior has to become the personal behavior of the client to weaken the prediction value. Customers also become overwhelmed with future predictions due to the large and monotonous daily entries, which detracts from the accuracy of the predictions due to the reluctance of customers, particularly business-internal handling customers. The business itself or the customers cannot judge their merits or their demerits because of the lack of quantitative historical evaluation of each customer for future prediction of a given benchmark index. The failure of this kind of judgment is the reason for the filling of the taro or the tearing of the upper and lower shells in the enterprise. As a result, conventional future forecasting and historical analysis not only may not help decisions of the enterprise or the customer, but may instead sometimes poison or reduce the efficiency of the enterprise's decisions due to poor experience operability.

Disclosure of Invention

The present invention has been made in view of the above circumstances, and an object thereof is to provide a system for performing future prediction and history analysis on an information index, which can efficiently perform future prediction and history analysis on an information index.

The invention provides a system for carrying out prediction analysis and display on an information index, wherein the information index is an array comprising historical, present and future data and formed in a visual or non-visual mode, and comprises a reference index serving as a calculation basis and a secondary index obtained by recalculating according to the reference index and a custom algorithm; the system comprises:

the reference index unit is used for displaying the third-party information index or the information index which is released by the customer; and determining the reference index, allowing an externally input information index to be determined as the reference index, or determining the customer's own internal data as a basic index;

the historical data processing unit is used for calculating, searching, displaying and uploading the historical data of the information index in different storage environments such as a client server, a third-party platform or a cloud end; after the third-party information index is calculated by combining the data of the client, the third-party information index is issued to a third-party platform for the client or other multiple clients to determine as the reference index;

the prediction and comparison unit is used for predicting the future information index and comparing and displaying the prediction results of a plurality of clients;

the data combination unit is used for forming the historical data of the information index or the information index set, the quantitative prediction data of the client, the qualitative analysis of the client or the client team and the final action countermeasure into iterative combined display of the information history, the prediction and the action countermeasure and providing the function of iterative combined display search in the past;

a prediction history analysis unit for counting, sorting and retrieving history predictions; calculating the average price difference between each prediction index data from the prediction date and the reference index data, and judging the capability of the client for predicting future index fluctuation according to the average price difference; calculating the trend accuracy of each prediction index curve from the prediction day and the reference index curve, and judging the energy of the future trend track of the client prediction index according to the trend accuracy. .

Further, the prediction and comparison unit includes:

a prediction index unit for predicting an information index by finger sliding at a moving end;

and the comparison display unit is used for comparing and displaying the results predicted by the plurality of clients.

Further, the prediction index unit is further configured to:

storing the prediction result of the client according to the storage node every day and the storage record in the storage node every day of the prediction result;

pushing single point prediction, recording and storing through sliding type single click;

quickly finding a prediction point with any precision by back-and-forth sliding;

rapidly estimating the shape and the predicted point of the curve with any length in the prediction interval;

finding the most approximate order unit according to the custom of the client;

the effective numerical value which can be slid to the actual use habit of the customer;

automatically matching an exponential curve display interval;

reminding and damping invalid sliding of the client beyond a reasonable range; .

Reminding the client of being in a predicted state or exceeding a reasonable range through sound effect;

the customer is provided with the choice of integer or arbitrary decimal as the predicted step size.

Further, the prediction and comparison unit is combined with the data combination unit to form the information history, prediction and action strategy iterative combined display by the historical data of the information index or the information index set, the quantitative prediction data of the client, the qualitative analysis of the client or the client team and the final action strategy.

Further, the prediction and comparison unit is combined with the data combination unit to be used for iterative combined display of the information history, prediction and action countermeasures and providing a past iterative combined display search function.

The present disclosure also provides a method for performing information index prediction analysis and display by using the system, which includes the following steps:

the client searches a plurality of third party price indexes by using the client for carrying out future prediction and historical analysis on the information indexes and determines the reference index concerned by the client;

the client judges whether the reference index is issued by the client;

if so, defining the relationship between the commodity and the information index, defining the distribution entry task, and designating a special person or a client to enter periodically;

the client establishes a template of quantitative relations among various information indexes, determines a reference index, and determines various dimensions and sequences which influence the reference index;

the client judges whether the reference index is only suitable for the client or the interior of the enterprise;

if yes, the customer selects internal communication;

if not, the customer selects other external customers or enterprises to predict together;

in the process of forecasting together with other external clients or enterprises, the clients prefer a more reliable information source and give an early warning to an unreliable information source, and continuously judge whether a closed discussion group mode needs to be entered or not;

based on the cognition of the client on the information index, the client continuously and preferably selects a reliable information source inside a client enterprise or between the client enterprise and gives an early warning to the unreliable information source, the future prediction is combined with the actual decision, and historical data and historical prediction records of the client are summarized;

the client updates the coupling relation among various information indexes, and predicts the information indexes to form a more systematic template and an operation flow corresponding to the actual business;

and in the actual operation of the client, the template and the operation flow are iteratively advanced according to whether the template and the operation flow are actually deviated or not.

Drawings

FIG. 1 is a schematic diagram of a system for future prediction and historical analysis of an information index according to the present invention;

FIG. 2 is a schematic view of an embodiment of the present invention;

FIG. 3 is a schematic view of an embodiment of the present invention;

FIG. 4 is a schematic representation of an embodiment of the present invention;

FIG. 5 is a schematic view of an embodiment of the present invention;

FIG. 6 is a schematic illustration of an embodiment of the present invention;

FIG. 7 is a schematic illustration of an embodiment of the present invention;

FIG. 8 is a schematic diagram of an embodiment of the present invention;

FIG. 9 is a schematic view eight of an embodiment of the present invention;

fig. 10 is a schematic illustration nine of an embodiment of the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings in conjunction with the following detailed description. It should be understood that the description is intended to be exemplary only, and is not intended to limit the scope of the present invention. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present invention.

As shown in fig. 1 to 10, the present invention provides a system for performing future prediction and history analysis on an information index, which can effectively perform future prediction and history analysis on the information index.

FIG. 1 is a schematic diagram of a system for future prediction and historical analysis of an information index according to the present invention.

As shown in fig. 1, a system for future prediction and history analysis of an information index, the system comprising:

a reference index unit 101, configured to display a third party information index or an information index issued by a client;

the historical data processing unit 102 is used for calculating, searching, displaying and uploading historical data of the information index in different storage environments such as a client server, a third-party platform or a cloud end;

the prediction and comparison unit 103 is used for predicting future information indexes and comparing and displaying a plurality of prediction results;

a data combination unit 104, configured to form an iterative merged display of information history, prediction and action countermeasures from historical data of the information index or the information index set, customer quantitative prediction data, customer or customer team qualitative analysis and final action countermeasures, and provide a past iterative merged display search function;

a prediction history analysis unit 105 for counting, sorting and retrieving the history predictions.

The information index is a set of data containing history, present and future, which are organized into arrays in a visualized or non-visualized manner.

The information index comprises a reference index used as a calculation basis and a secondary index calculated again according to the reference index and a custom algorithm.

The historical data processing unit 102 is further configured to calculate the third-party information index in combination with the data of the client itself, and then distribute the third-party information index to the third-party platform for the client itself or other multiple clients to determine as the reference index.

The prediction and comparison unit 103 includes:

a prediction index unit for predicting an information index by finger sliding at a moving end; .

Further, the prediction index unit is further configured to:

finding the most approximate order unit according to the custom of the client;

automatically matching an exponential curve display interval;

reminding and damping invalid sliding of the client beyond a reasonable range;

The prediction and comparison unit 103 is combined with the data combination unit 104 to form an iterative combined presentation of information history, prediction and action countermeasures by historical data of the information index or the information index set, customer quantitative prediction data, customer or customer team qualitative analysis and final action countermeasures.

The prediction and comparison unit 103, in combination with the data combination unit 104, is further configured to iteratively merge and present the information history, prediction and action countermeasures, and provide a past iterative merged presentation search function.

The prediction history analysis unit 105 is further configured to:

calculating the average price difference between each prediction index data and the reference index data from the prediction day;

judging the capability of the customer for predicting future index fluctuation through the average price difference;

calculating the trend accuracy of each prediction index curve and a reference index curve from the prediction day;

and judging the capability of the customer to predict the future trend track of the index through the trend accuracy.

The prediction history analysis unit 105 is also used for providing the customer with a history of inquiring the prediction mean price difference and the trend accuracy of the customer in the past and reviewing the prediction capability of the customer.

Example one

The benchmark index unit 101 is responsible for letting the client determine a benchmark index, allowing externally input information indexes to be determined as the benchmark index, or determining the client's own internal data as the basic index. The base index may be for self-study, for internal distribution within a company, or for distribution to society. In this embodiment, the reference index is derived from an information index issued by the client itself. This informational index is received by the historical data processing unit 102 of the plurality of customers so that other customers perceive the informational index as coming from a third party platform and being determined by the plurality of customers as respective benchmark indices.

Generally, there are many client modes capable of separately receiving and presenting the third party information index, but most of these clients only passively receive data communication and data presentation, and more advanced BI can provide integration of the information index, but does not provide a publishing function, and even does not provide a comparison function for multiple clients inside and outside a company. However, in this embodiment, the "received multiple information indexes" or "information indexes released by the client itself" is used as a starting point, and is combined with the future prediction and history analysis of the client to form an active working mode, and then the client performs targeted distribution (i.e. limited clients) or overall distribution (i.e. any client), so that the multiple clients have the same reference index. The result of this structure is that the information index is not necessarily controlled by a single client, but rather can be studied by multiple clients associated with the business to play quantitatively. The information index determined by the reference index unit 101 is displayed at the mobile phone end, as shown in fig. 2. In fig. 2, the "pure benzene high-bridge factory price" from the third party is combined with the 50-yuan discount obtained from the actual operation of the customer, which is the actual purchase cost of the customer, and the information index "pure benzene east china low end" is formed after being issued by the reference index unit 101. Secondly, the daily change data of the 'pure benzene east China Low end' information index is calculated, searched, displayed or uploaded again through the respective historical data processing units 102 of a plurality of clients in a storage environment of a third-party platform such as an SAAS mode.

The combination of the reference index unit 101 and the historical data processing unit 102 enables a plurality of clients to realize a cyclic process or a synchronous process of receiving, processing and releasing the information index under a plurality of storage environments. The information processing behaviors at the individual level or the enterprise level in the enterprise can be simultaneously satisfied.

Further, the reference index unit 101 and the historical data processing unit 102 also provide a cyclic process or synchronization process of receiving-processing-publishing of a group of information indexes (called templates) composed of different sets of information indexes, as shown in fig. 3. Through the function, the client can inertially realize the multi-client and multi-authority sharing of the historical data set on the enterprise client, the third-party platform or the cloud without updating the data every day.

After the reference index is determined, a client can freely select to obtain the latest change of the reference index through the client under different storage environments such as a client server, a third-party platform or a cloud end according to enterprise-level or personal requirements. Typically, most customers simply want to see the daily updates of the baseline index, so the historical data processing unit 102 provides default daily updates. By combining with the customer's future prediction data implemented in the prediction and comparison unit 103, the customer can see both the "pure east-west-benzene-east-low" historical data or curves and the customer's future predicted data and curves, respectively, in the historical data processing unit 102, and can also see a combined presentation of the data and curves of two disparate data sources, as shown in fig. 4. The above two different forms of displays can be calculated, searched, displayed and uploaded in a combined or non-combined manner according to different time periods of historical natural months, first-to-current days of the current natural month, the current natural month and future prediction intervals. In this embodiment, as shown in fig. 4, X1, which is the average price of 7 months, is shown for "pure benzene east china low end" and is not combined with the customer's future prediction; x2, average price from first month to day of month 8 of the current day. The prediction method can also be used for predicting the combination of the prediction of the current day of 8 months to the residual 8 months by the client in a combination mode X2+ X3, namely under the condition that the reference index of X2 is available, and the like.

The historical data processing unit 102 is associated with a prediction and comparison unit 103, both of which enable communication to be cleared. When the customer has no time to make a prediction and comparison using the prediction and comparison unit 103 because the work is busy, the data of the history data processing unit 102 secures the operability of the data combination unit 104. After the customer uses the prediction and comparison unit 103 to perform prediction and comparison, the historical data processing unit 102 automatically obtains the prediction data generated by the prediction and comparison unit 103 to meet the functional requirements of the customer.

Some stock, fund and futures software have tools for clients to predict the future and may also trace various possible curves. However, such software requires input data for the curve plotting by means of data entry. The prediction and comparison unit 103 provides a function of describing future prediction data and figures that the customer is satisfied with by merely sliding a finger at the mobile terminal without typing any number. And, in the process of finger sliding describing future prediction data and curves, the algorithm of the prediction and comparison unit 103 helps the customer solve several experience problems: no matter how fast the screen of the mobile phone of the client responds, the predicted data and curves on the mobile phone can be changed through the minimum number of clicks, the data in a single day can be modified, the data and curves in any time period in the screen can be modified through clicking for at most three times, the client can be ensured to obtain the most approximate data and curves according to the daily variation range of different data, the most accurate data and curves can be obtained according to the minimum effective numerical range displayed on the screen, and sound effect friendly prompts are given when the finger of the client slides out of the data and curves with overlarge or undersize daily fluctuation. In summary, the innovations in the above experiences are all for the purpose of enabling the customer to describe future data and graphics of the reference index with only fingers and in the most time-saving and labor-saving way.

The system forms the historical data of the information index or the information index set, the quantitative prediction data of the client, the qualitative analysis of the client or the client team and the final action strategy into the iterative merged display of the information history, the prediction and the action strategy through the data combination unit 104, and provides the search function aiming at the past iterative merged display for the iterative merged display of the information history, the prediction and the action strategy, as shown in fig. 5. In this example, Zhang III, Li Si and Wang Wu respectively present their own insights on a template consisting of "pure benzene east China Low end" and a series of indices related to this basic index. The insight emerges above the template and is shown merged with the historical data in the template. Since such knowledge distribution can be done daily, these knowledge and data are iteratively progressive. Therefore, if the user wants to trace back historical data, participating clients, qualitative or quantitative prediction, and action data of a certain past period, the user can find the data by the search function to perform so-called reply analysis. After Zhang three, Li four and Wang five are shared, the prediction data can be displayed simultaneously in a multi-client mode. If zhang san job is busy and no guess is made at 8 months and 15 days, the data combining unit 104 provides a service that the default price curve iteratively advances after selecting the data of the previous day, thereby avoiding the labor that the person responsible for the information index prediction in the past must have daily readiness.

After the low end of pure benzene east China is determined as a reference index and predicted by Zhang three, Li four and Wang five respectively, the prediction history analysis unit 105 is responsible for recording the deviation of the average price difference and the curve trend accuracy of three persons since the prediction of the low end of pure benzene east China is started, as shown in FIG. 6, the historical average deviations of Zhang three, Li four and Wang five for the average price difference are x, y and z respectively; the trend accuracies are respectively L2a, L2b and L2 c. Therefore, the client automatically counts and calculates the level of future prediction of different clients for the same benchmark index, and automatically notifies interested third-party clients according to client selection. The formula for calculating the historical average deviation of the mean deviation is as follows:

recording the error generated by the historical date (month, day) as Δ (month, day), recording pa (X) as a function of calculating the average value of the absolute value of the price index of the X set interval, and recording the average value of the absolute value of the historical average price difference as the historical average price deviation, wherein the historical average price deviation is pa (X) ∑ Δ (month, day) |/(day-enabling day).

Example two

Fig. 7 shows a block diagram of a client for implementing future prediction and history analysis of information index according to a second embodiment of the present invention. For convenience of explanation, only portions related to the embodiments of the present invention are shown. The difference between the information index future prediction and history analysis client provided by the first embodiment is that: (1) the client directly determines the third party information index as the basic index. (2) The client uses a client server, i.e., typically a large enterprise with its own server.

In this embodiment, after the information index from the third party is determined as the reference index, the reference index unit 101 communicates with the history data processing unit 102 located in the client server. Thus, the client on the client server can display the information scene from the benchmark index unit 101, as in fig. 8.

In fig. 8, after the index "bisphenol a asian index" from the third party information is determined as the reference index by the reference index unit 101, the data is compatible with the future prediction data from the prediction and comparison unit 103, i.e., the bisphenol a aftermarket prediction with the attribute of "guess", in the historical data processing unit 102.

This information index may be received by the historical data processing unit 102 of multiple customers within the company at the same time, so that other customers perceive the information index as coming from a third party platform and being determined by the multiple customers as respective benchmark indexes.

In this second embodiment, the baseline indexing unit 101 and the historical data processing unit 102 still store and display a customer-initiated template containing the "bisphenol a asian index," as shown in fig. 8. After the template is determined, the historical data is conveniently shared by other clients in the company through the historical data processing unit 102, and multi-platform data communication is achieved. Furthermore, the benchmark index 'bisphenol A Asian index', BPA post-market prediction and large customer sales tracking data customized by a certain customer can be merged and displayed, so that historical data, future prediction and actual sales data are communicated.

Similar to the embodiment, in the second embodiment, if the "chat" flag in fig. 8 is pulled up, it can be seen that under the control of the data combination unit 104, under the mapping of the historical data of the template, the client team can perform qualitative or quantitative operations for future prediction. After a plurality of times, the combined display and search of past historical data, future prediction, qualitative or quantitative operation can be realized, and the repeated analysis is realized.

Fig. 9 shows an implementation flow of the method for performing future prediction and history analysis on an information index and using the information index prediction and history analysis client shown in fig. 1, which is detailed as follows:

in step S101, the client searches for a plurality of third party price indices using the client that performs future prediction and history analysis on the information index, and determines a reference index that the client cares about. The benchmark index plays a decisive role in the actual operation work of the client or the enterprise where the client is located. The various events that the customer selects to affect the benchmark index, or the informational index that the event represents, is defined as a dimension. These dimensions can be either external or internal factors.

In step S102, the client determines whether or not the reference index is issued by itself. If yes, the method goes to step S103, the relation between the commodity and the information index is defined, an allocation entry task is defined, and a specially-assigned person or a client is appointed to enter periodically; if not, the reference index is directly selected from the third party price indexes.

After the judgment at S102, the customer proceeds directly to step S104 with or without S103. In step S104, the customer will enumerate the dimensions periodically and then sort the importance, and the customer or other responsible person will perform operations such as entering, sharing, calculating, and displaying the data of the index information in a daily, weekly, monthly, and other manners. The customer determines the reference index and various dimensions and sequences which have influences on the reference index by establishing a template of quantitative relations among various information indexes.

In step S105, the client determines whether the reference index is applicable only to the client itself or the inside of the enterprise, or shared and communicated with other external clients or the enterprise, and improves the prediction effect by the communication.

In step S106, the customer has selected to make predictions with other outside customers or businesses B, C. A study population of open informative indices or templates is formed.

In step S107, the customer selects internal communication to form a closed type discussion group.

In step S108, since the information exchange between the enterprises is realized, the client may spend time preferring more reliable information sources and early warning unreliable information sources. If the external client or enterprise B, C is valuable, the client may view them as a reliable source of information. Otherwise, the customer can close the sharing with B, C at any time and review himself to enter the closed workshop mode.

In step S109, the client continuously and preferably selects the knowledge of the client itself on the information index, the reliable information source inside the client enterprise or between the enterprise and the enterprise, and gives an early warning to the unreliable information source, so that the future prediction is combined with the actual decision, and the historical data and the historical prediction record of the client are summarized and reasoned, that is, the review analysis is performed. Theory is connected with reality.

In step S110, the customer updates the coupling relationship among various information indexes, and the prediction of the information indexes forms a more systematic template and an operation flow corresponding to the actual business.

In step S111, the client determines whether the template and the operation flow deviate from the actual operation. If the deviation occurs, returning to the step S109, and performing repeated analysis; then, S110, updating the coupling relationship among various information indexes, and predicting the information indexes to form a more systematic template and an operation flow corresponding to the actual service. This forms an iterative progression. If no deviation occurs, the automatic driving state that the template and the process are automatically combined with the actual decision is kept, and finally the option returns to the client.

To describe the client for performing future prediction and historical statistics on the information index shown in fig. 1 in detail, the following describes, by taking the client for performing future prediction and historical statistics on the information index shown in fig. 1 as an example, a method for performing future prediction and historical statistics on the information index and the historical statistics client shown in fig. 1 in detail, as shown in fig. 10:

1. and performing future prediction on the information index, searching third party indexes such as 'medium petrochemical factory price' and the like by a basic index unit of a historical statistics client according to the requirement of the client for issuing the index, and preparing for building a template.

2. And starting the historical data processing unit, selecting the relation between the commodity and the information index on a third-party platform by a client, defining the optimal dimension, defining, distributing and inputting tasks and issuing the tasks.

3. In the prediction and comparison unit, the client A establishes a discussion group by itself and selects other clients to initiate collective prediction.

4. The prediction comparison unit combines the information index prediction with the actual decision under the operation of the client, and presents historical data and historical prediction records of the client.

5. The data combination unit receives the data from the prediction and comparison unit, and updates the coupling relation among various indexes, thereby updating the presentation of the data.

6. And the data combination unit reminds the client of updating the template and the operation flow of the actual business for the combination of the question or the failure case in the display.

7. And the data combination unit outputs data to the prediction history analysis unit to prepare for judging whether the template and the operation flow deviate from the actual deviation.

8. The prediction history analysis unit enables template sharing, prediction history analysis statistics to be automatically combined with actual decision making, and finally the weight is selected to return to the client.

It is to be understood that the above-described embodiments of the present invention are merely illustrative of or explaining the principles of the invention and are not to be construed as limiting the invention. Therefore, any modification, equivalent replacement, improvement and the like made without departing from the spirit and scope of the present invention should be included in the protection scope of the present invention. Further, it is intended that the appended claims cover all such variations and modifications as fall within the scope and boundaries of the appended claims or the equivalents of such scope and boundaries.

Claims

1. A system for carrying out prediction analysis and display on an information index is characterized in that the information index is an array comprising historical, present and future data and formed in a visual or non-visual mode, and comprises a reference index used as a calculation basis and a secondary index obtained by recalculation according to the reference index and a custom algorithm;

the system comprises:

the reference index unit is used for displaying the third-party information index or the information index which is released by the customer; and

determining the reference index, allowing an externally input information index to be determined as the reference index, or determining the internal data of the client as the reference index;

a prediction history analysis unit for counting, sorting and retrieving history predictions; calculating the average price difference between each prediction index data from the prediction date and the reference index data, and judging the capability of the client for predicting future index fluctuation according to the average price difference; calculating the trend accuracy of each prediction index curve from the prediction day and the reference index curve, and judging the capability of the client for predicting the future trend track of the index according to the trend accuracy.

2. The system of claim 1, wherein the prediction and comparison unit comprises:

3. The system of claim 2, wherein the prediction index unit is further configured to:

finding the most approximate order unit according to the custom of the client;

automatically matching an exponential curve display interval;

reminding and damping invalid sliding of the client beyond a reasonable range;

4. The system of claim 1, wherein the prediction and comparison unit is combined with the data combination unit to form an iterative merged presentation of information history, prediction and action countermeasures from historical data of the information index or set of information indices, customer quantitative prediction data, customer or customer team qualitative analysis, and final action countermeasures.

5. The system of claim 4, wherein the prediction and comparison unit in combination with the data combination unit is further configured to iteratively merge presentations of the information history, prediction and action countermeasures and provide a past iterative merged presentation search function.

6. A method for performing index of information predictive analysis and presentation using the system of any one of claims 1-5, comprising the steps of:

the client judges whether the reference index is issued by the client;

the client establishes a template of quantitative relations among various information indexes, determines a reference index, and sorts various dimensions and dimensions which have influences on the reference index;

if yes, the customer selects internal communication;