CN106959938A - A kind of data average growth rate computational methods and device - Google Patents
A kind of data average growth rate computational methods and device Download PDFInfo
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Abstract
The present invention provides a kind of data average growth rate computational methods and device, and methods described includes：Obtain the predicted time for assessing object to each target data；Numerical value and described each target data numerical value in the scheduled time of each target data in the predicted time is obtained respectively, wherein the scheduled time is after the predicted time；The average growth rate of object institute prediction data is assessed according to the numerical computations of numerical value of each the described target data in the predicted time and each described target data in the scheduled time.
Description
Technical field
Excavated the present invention relates to computer data and process field, and in particular to a kind of data average growth rate computational methods
And device.
Background technology
With the development of computer and Internet technology, the intelligent of computer processing data has obtained significant raising.
At present, computer can be estimated according to predetermined rule or algorithm or predict some data in following change.
Such as some data can be varied in itself, and their change procedure is nonlinear, or not
Meet the rule of low dimensional.If the change of these data can be to related industry bad student's significant impact, then predict that these are counted
According to change be then very necessary, the future trend of pre- primary data helps to make counte-rplan in advance.Assuming that in the presence of one
Machine or someone can be predicted to the change of these data, no matter using the deep learning algorithm performed by computer
Still artificial experience is relied on, is all difficult to predict specific numerical value, and is typically only capable to enough provide a kind of trend prediction, namely in advance
Survey some data it is following be for a period of time increase, it is constant or reduce.Because data prediction side would generally be to substantial amounts of data
It is predicted, so user is difficult the confidence level for a large amount of conclusions for verifying data prediction side, namely can not evaluates or determine data
The performance of prediction side.
The content of the invention
Therefore, the technical problem to be solved in the present invention is performance the asking of can not being evaluated or determine of data prediction side
Topic.
In view of this, the present invention provides a kind of data average growth rate computational methods, including：
Obtain the predicted time for assessing object to each target data；
The numerical value and described each target data of each target data in the predicted time is obtained respectively to exist
The numerical value during scheduled time, wherein the scheduled time is after the predicted time；
According to numerical value of each the described target data in the predicted time and each described target data predetermined
The average growth rate of object institute prediction data is assessed described in numerical computations during the time.
Preferably, it is described obtain each described target data in the predicted time respectively when numerical value and described each
After numerical value of the individual target data in the scheduled time, also include：
Respectively according to the numerical value of each target data in the scheduled time with each described target data described pre-
The growth rate of each target data described in numerical computations during the survey time；
Numerical value and each described target data when each target data is in the predicted time described in the basis
After the average growth rate that object institute prediction data is assessed described in numerical computations in the scheduled time, also include：
The prediction knot for assessing object is calculated according to the growth rate of each target data and the average growth rate
The fluctuation of fruit.
Preferably, the growth rate of each target data described in the basis calculates the assessment pair with the average growth rate
The fluctuation predicted the outcome of elephant, including：
The fluctuation is calculated using following formula：
D=1/n ((X1-S)2+(X2-S)2+(X3-S)2+……+(Xn-S)2),
Wherein D is the fluctuation, and X1, X2, X3 ... Xn are the growth rate of each target data, and S is described flat
Equal growth rate, the quantity for the target data that n is predicted by the assessment object.
Preferably, the assessment object is to grind to report data publisher, the target data to grind count off according to involved to be described
The market value of stock, the predicted time are the issuing time for grinding count off evidence；
The predicted time that assessment object is obtained to each target data, including：
Acquisition is multiple to grind count off evidence, and the count off evidence of grinding includes distributor information and the trend prediction letter to the market value of stock
Breath and issuing time；
From it is the multiple grind count off filtered out in trend that the tendency prediction information indicates for it is positive increase grind report
Data；
The count off of grinding increased according to the distributor information from forward direction determines that the assessment object publishing grinds count off in
According to；
Respectively issuing time is determined from the count off of grinding of determination in.
Preferably, also include：
According to numerical value of the stock index in predicted time and the scheduled time of each target data
Calculate the average growth rate of the stock index；
Numerical value and each described target data when each target data is in the predicted time described in the basis
After the average growth rate that object institute prediction data is assessed described in numerical computations in the scheduled time, also include：
The average growth rate for the average growth rate and stock index for reporting data publisher to predict stock is ground in calculating
Difference, the earning rate of data publisher is reported as described grind.
Correspondingly, the present invention also provides a kind of data average growth rate computing device, including：
Acquisition module, for obtaining the predicted time for assessing object to each target data；
Numerical value determining module, for obtaining the numerical value and institute of each target data in the predicted time respectively
Numerical value of each target data in the scheduled time is stated, wherein the scheduled time is after the predicted time；
Mean value calculation module, for according to numerical value of each the described target data in the predicted time and described
The average growth rate of object institute prediction data is assessed described in numerical computations of each target data in the scheduled time.
Preferably, also include：
Growth Rate Calculation module, for after numerical value determining module processing, respectively according to each described number of targets
According to the numerical value in the scheduled time with each described target data in the predicted time when numerical computations described in each target
The growth rate of data；
Fluctuation computing module, for after mean value calculation resume module completion, according to each described target
The growth rate of data calculates the fluctuation predicted the outcome for assessing object with the average growth rate.
Preferably, the fluctuation computing module calculates the fluctuation using following formula：
D=1/n ((X1-S)2+(X2-S)2+(X3-S)2+……+(Xn-S)2),
Wherein D is the fluctuation, and X1, X2, X3 ... Xn are the growth rate of each target data, and S is described flat
Equal growth rate, the quantity for the target data that n is predicted by the assessment object.
Preferably, the assessment object is to grind to report data publisher, the target data to grind count off according to involved to be described
The market value of stock, the predicted time are the issuing time for grinding count off evidence；
The acquisition module includes：
Grind report data acquisition module, for obtain it is multiple grind count off evidence, it is described grind count off according to include distributor information with
To the tendency prediction information and issuing time of the market value of stock；
Forward direction grinds count off according to screening module, for from it is the multiple grind count off and filter out the tendency prediction information in refer to
The trend shown grinds count off evidence for positive growth；
Publisher's screening module, the count off of grinding for being increased according to the distributor information from forward direction determines institute's commentary in
That estimates object publishing grinds count off evidence；
Information of forecasting acquisition module, for determining issuing time in from the count off of grinding of determination respectively.
Preferably, also include：
Stock index average growth rate determining module, for according to stock index in the prediction of each target data
Between and the numerical value during scheduled time
Calculate the average growth rate of the stock index；
Earning rate computing module, for after mean value calculation module completion processing, report data publication to be ground in calculating
Person predicts the difference of the average growth rate of stock and the average growth rate of the stock index, and report data publication is ground as described
The earning rate of person.
The data average growth rate computational methods and device provided according to embodiments of the present invention, obtain assess object pair first
The predicted time of each target data, it is then determined that numerical value of each target data in the predicted time and latter section
The numerical value during time, after being verified to a large amount of prediction data that same assessment object is provided, can calculate the assessment pair
The average growth rate of all prediction objects of elephant, this data can reflect the confidence level for assessing object, it is possible to be used as mesh
The important references index of data consumer is marked, so as to assist user to assess or determine the performance of data prediction side, and then assists to use
Counte-rplan are made in change of the family to data in advance.
Brief description of the drawings
, below will be to specific in order to illustrate more clearly of the specific embodiment of the invention or technical scheme of the prior art
The accompanying drawing to be used needed for embodiment or description of the prior art is briefly described, it should be apparent that, in describing below
Accompanying drawing is some embodiments of the present invention, for those of ordinary skill in the art, before creative work is not paid
Put, other accompanying drawings can also be obtained according to these accompanying drawings.
Fig. 1 be one embodiment of the present of invention in data average growth rate computational methods flow chart；
Fig. 2 be an alternative embodiment of the invention in data average growth rate computational methods flow chart；
Fig. 3 for the present invention the 3rd embodiment in data average growth rate computing device structural representation.
Embodiment
Technical scheme is clearly and completely described below in conjunction with accompanying drawing, it is clear that described implementation
Example is a part of embodiment of the invention, rather than whole embodiments.Based on the embodiment in the present invention, ordinary skill
The every other embodiment that personnel are obtained under the premise of creative work is not made, belongs to the scope of protection of the invention.
In the description of the invention, it is necessary to which explanation, term " first ", " second " are only used for describing purpose, and it is not intended that referring to
Show or imply relative importance.
As long as in addition, technical characteristic involved in invention described below different embodiments non-structure each other
It can just be combined with each other into conflict.
The embodiments of the invention provide a kind of data average growth rate computational methods, as shown in figure 1, this method is including as follows
Step：
S01, obtains and assesses object to the predicted time of each target data, assessment object therein can be computer
Can be people, target data is to assess the prediction object of object.This method can apply to a variety of industries and field, different necks
The content of target data is differed in domain, but the value of target data must be anaplasia at any time, is, for example, certain with time loss
Or growth parameter.Predicted time refers to assess the object publishing this time predicted the outcome.In order to more clearly to this
Scheme is illustrated, and a concrete application scene is enumerated below, such as in field of batteries, computer can join according to discharge and recharge
The electric quantity change situation of the multiple battery cells of number prediction, then battery electric quantity is target data, it is assumed that battery 1 is at the t1 moment
Electricity is a%, computer moment t1 according to charge-discharge parameter predict the electricity of the battery 1 to the t2 moment be growth, reduce or
It is constant；Electricity of the battery 2 at the t3 moment is c%, and computer predicts that the electricity of the battery 2 is arrived in moment t3 according to charge-discharge parameter
The t4 moment is growth, reduction or constant, and its Computer is assessment object, and the moment, t1 and t3 were predicted time.
S02, obtains numerical value and each target data of each target data in predicted time in the scheduled time respectively
When numerical value, the wherein scheduled time is after predicted time.The signified scheduled time t2 of the present embodiment must be at the above-mentioned moment
After t1, time t4 must be after above-mentioned moment t3, it is assumed that t4 is a time point most rearward, then this method is at least needed
To be performed after scheduled time t4, namely the present invention needs numerical value and target data of the hard objectives data at the t1 moment to exist
The numerical value at t2 moment, and target data the t3 moment numerical value and target data the t4 moment numerical value.Wherein pre- timing
Between can be determined according to predicted time, a few minutes, a few houres, several days e.g. after predicted time even some months all
It is feasible, specific needs are set according to the property of target data.Thus, same assessment object is to different target datas
Predicted time can differ, for same target predict different target data, the present invention selected by the scheduled time
Can also be different.
S03, according to numerical value of each target data in the predicted time and each target data in the scheduled time
When numerical computations assess the average growth rate of object institute prediction data, the growth rate can be positive number, negative or 0.For example
It is that numerical value of the electricity of b%, battery 2 at the t4 moment is to get numerical value of the electricity of battery 1 at the t2 moment by above-mentioned steps
D%, calculate average growth rate mode have a variety of, for example average growth rate can for [(d%+b%)-(a%+c%)]/
((a%+c%)) }.
The data average growth rate computational methods provided according to embodiments of the present invention, obtain assess object to each mesh first
The predicted time of data is marked, it is then determined that numerical value of each target data in the predicted time and after during a period of time
Numerical value, after being verified to same a large amount of prediction data for providing of assessment object, the institute of the assessment object can be calculated
There is the average growth rate of prediction object, this data can reflect the confidence level for assessing object, it is possible to be used as target data
The important references index of user, so as to assist user to assess or determine the performance of data prediction side, and then assists user's logarithm
According to change make counte-rplan in advance.
As one preferred embodiment, after above-mentioned steps S02, this method can also comprise the following steps：
S04, respectively according to numerical value of each target data in the scheduled time with each target data in the prediction when
Between when numerical computations described in each target data growth rate, the growth rate can be positive number, negative or 0, by above-mentioned
Step can calculate the growth rate of N number of target data.Still by taking above-mentioned application scenarios as an example, battery 1 can be calculated respectively
The growth rate of electricity is (b%-a%)/a%, and the growth rate of the electricity of battery 2 is (d%-c%)/c%；
After above-mentioned steps S03 and S04, this method can also comprise the following steps：
S05, the fluctuation predicted the outcome for assessing object is calculated according to the growth rate of each target data and average growth rate
Property.
The real growth rate of the average growth rate calculated according to above-mentioned steps and each target data is to there may be difference
It is different, and it is possible that situation about differing greatly, for example assess object and have altogether and predicts N number of data, average growth rate for+
R%, the growth rate of which part data has the growth rate of partial data and+R% to differ greatly close to+R%, or even goes out
Existing negative value, then ratio shared by the larger result of the size and otherness of otherness can impact evaluation object it is overall steady
It is qualitative.Specifically calculating the mode of otherness has a variety of, such as is all feasible using some existing mathematical algorithms.This implementation
Example provides a kind of preferred calculation, i.e. above-mentioned steps S05 can specifically be calculated as follows：
D=1/n ((X1-S)2+(X2-S)2+(X3-S)2+……+(Xn-S)2),
Wherein D is the fluctuation, and X1, X2, X3 ... Xn are the growth rate of each target data, and S is described flat
Equal growth rate, the quantity for the target data that n is predicted by the assessment object.
Aforesaid way respectively subtracts each other the growth rate of each target data and average growth rate, and result then is done into a square fortune
Calculate, finally with the summation of square operation result divided by the number of target data, ripple of the result as data prediction result will be obtained
Dynamic property.Above-mentioned preferred scheme considers the single difference predicted the outcome with average growth rate, has thus reflected the stabilization of prediction side
Property, it further have evaluated the performance of prediction side.
It is above an embodiment for being applied to field of batteries, it is computer or software that object is assessed in this embodiment
Program, procedural model or program module.Another application scenarios are provided below, an alternative embodiment of the invention additionally provides one
Data average growth rate computational methods are planted, in the present embodiment, it is to grind to report data publisher (to be also referred to as stock analysis to assess object
Teacher or analyst), target data be that to grind count off according to the market value of involved stock, predicted time be the issuing time for grinding count off evidence.
Specifically, as shown in Fig. 2 this method comprises the following steps：
S21, acquisition is multiple to grind count off evidence, and grinding count off evidence includes distributor information and the trend prediction letter to the market value of stock
Breath and issuing time.Acquisition modes can for example be scanned for including a variety of in predetermined some websites, server, with to the greatest extent
It may collect and all grind count off evidence.This step puts aside data particular content, only determines whether to grind count off according to example
It can such as be screened by file name, file type attribute.
S22, from it is multiple grind count off filtered out in the trend of tendency prediction information instruction for it is positive increase grind count off evidence.
In order to reduce amount of calculation, the present embodiment filters out trend and grinds count off evidence for positive growth first, will no longer be to trend subsequently
It is neutral or drop to grind count off according to being judged.5 kinds generally are up to the anticipation trend of the market value of stock, also will in the art
Referred to herein as to the grading of stock, for example it is some grind count off according to use " buying in ", " increasing is held ", " neutrality ", " reducing ", " selling " this 5
The anticipation trend to the market value of stock is expressed in individual word, it is clear that wherein " buy in " and " increasing is held " represents be it is positive increase become
Gesture.Different institutions issue the form for grinding count off evidence be probably it is different, and word therein be also likely to be it is different, for example
Some count off evidences of grinding are also possible to coefficient of utilization (numerical value) expression trend.In this step, you can with using the side of keyword match
Formula determines anticipation trend, can also combine mode that predetermined threshold value matches determine anticipation trend.Grind count off evidence form and
Expression way meets certain professional standard, therefore expression way is also limited, it is possible to for from different institutions
Grind count off and filtered out according to corresponding identification method is taken trend for it is positive increase grind count off evidence.
S23, the count off of grinding increased according to distributor information from forward direction determines that assessing object publishing grinds count off evidence, example in
If current assessment object is analyst a, then the count off of grinding increased from these forward directions finds out author for analyst a in and grinds report
Data；
S24, determines issuing time from the count off of grinding of determination in respectively, and such as a issued 10 positive growth trends
Count off evidence is ground, then the count off of grinding respectively from this 10 positive growth trends determines issuing time in.
S25, obtains numerical value and each market value of stock of each market value of stock when grinding report issuing time predetermined respectively
The numerical value during time, the wherein scheduled time are after report issuing time is ground.Such as scheduled time can grind the 5 of report issuing time
After day, one month, half a year, 1 year.It can also be the day of trade, thus this implementation consecutive days that date in the present embodiment, which can be,
Example can also add the judgement operation to the date, specifically, it is assumed that the scheduled time in the present embodiment is grind report issuing time 5
The day of trade after day, thus it is determined that during target date, can first determine whether to judge 5 days that grind report issuing time it
Whether it is afterwards the day of trade, if not the day of trade near the date can be taken the day of trade, for example, takes nearest before the date
A day of trade.Can for example get stock 000001 its grind report issuing time market value be x1, the scheduled time again
Secondary its market value that gets is that y1, stock 00002 are x2 in its market value for grinding report issuing time, and it is got again in the scheduled time
Market value is y2.
S26, according to numerical value of each market value of stock in the scheduled time with each market value of stock in predicted time when number
Value calculates analyst and predicts the average growth rate of stock, and what such as analyst a issued N number of positive growth grinds count off evidence, then
Its average growth rate is { (Y1+Y2+Y3+........Yn)-(X1+X2+X3+.....Xn) }/(X1+X2+X3+.....Xn),
Wherein Y1+Y2+Y3+........Yn is market value of each stock in the corresponding scheduled time, and X1+X2+X3+.....Xn is each
Market value of the individual stock when accordingly grinding report issuing time.
The data average growth rate computational methods provided according to embodiments of the present invention, obtain analyst to each stock first
The predicted time of market value, it is then determined that numerical value of each market value of stock in the predicted time and after during a period of time
Numerical value, by same analyst is provided it is a large amount of grind count off according to verifying after, can calculate what the analyst analyzed
The average growth rate of all market value of stock, this data can reflect the confidence level of analyst, it is possible to be used as investor's
Important references index, so as to assist investor to assess or determine the professional skill of analyst, and then assists investor to stock city
Counte-rplan are made in the change of value in advance.
As one preferred embodiment, after above-mentioned steps S25, it can also comprise the following steps：
S27, respectively according to numerical value of each market value of stock in the scheduled time with each market value of stock in predicted time when
Numerical computations each market value of stock growth rate, can for example calculate herein the growth rate of stock 000001 for (y1-x1)/
X1, the growth rate of stock 000002 are (y2-x2)/x2.
It can also comprise the following steps after above-mentioned steps S25 and S27：
S28, the fluctuation predicted the outcome of analyst is calculated according to the growth rate of each market value of stock and average growth rate.
The present embodiment provides a kind of preferred calculation, i.e. above-mentioned steps S28 can specifically be calculated as follows：
D=1/n ((X1-S)2+(X2-S)2+(X3-S)2+……+(Xn-S)2),
Wherein D is fluctuation, and X1, X2, X3 ... Xn are the growth rate of each market value of stock, and S is average growth rate, and n is
The quantity for the stock that analyst is predicted.Such as analyst a is issued altogether grinds count off amount for 5 parts (being predicted 5 stocks),
Every part of growth rate for grinding report is respectively 25%, 10%, 20%, 15%, 25%, and average growth rate is (25%+10%+20%+
15%+25%)/5=20%.Thus, its fluctuation predicted the outcome is ((25%-20%)2)+(10%-20%)2)+
(20%-20%)2)+(15%-20%)2)+(25%-20%)2)/5=1.75%.
Aforesaid way respectively subtracts each other the growth rate of each market value of stock and average growth rate, and result then is done into a square fortune
Calculate, finally with the summation of square operation result divided by the number of the market value of stock, result will be obtained as the fluctuation predicted the outcome.
Above-mentioned preferred scheme considers the single difference predicted the outcome with average growth rate, has thus reflected predicting the outcome for analyst
Stability.
As one preferred embodiment, after above-mentioned steps S28, this method can also comprise the following steps：
S29, refers to according to numerical computations stock of the stock index in predicted time and the scheduled time of each target data
Several average growth rates.By taking Index of Shanghai Stock Exchange as an example, because analyst is different to the predicted time of each stock, so Index of Shanghai Stock Exchange phase
Growth pattern for each stock is different, can specifically be confirmed as follows：Determine analyst to each respectively
The predicted time of the market value of stock, then it is determined that Index of Shanghai Stock Exchange is in each predicted time and the numerical value of the scheduled time.For example analyze
Teacher a is t1 to the predicted time of stock 000001, and the scheduled time is t2, it is then determined that Index of Shanghai Stock Exchange is xa in t1, in t2
For ya；Analyst a is t3 to the predicted time of stock 000002, and the scheduled time is t4, it is then determined that Index of Shanghai Stock Exchange is in t3
Xb, is yb in t2.The calculation of the average growth rate of stock index has a variety of, for example, can be { (ya+yb)-(xa+
xb)}/(xa+xb)。
The average growth rate of the average growth rate for reporting data publisher to predict stock and stock index is ground in S30, calculating
Difference, as grinding the earning rate of reporting data publisher, such as balanced growth for the market value of stock that the analyst a calculated is predicted
Rate is+20%, and the average growth rate of the Index of Shanghai Stock Exchange calculated is+3%, then determined after subtracting each other the earning rate of analyst for+
17%.
Above-mentioned preferred scheme binding analysis teacher predicts that the average growth rate of stock and the average growth rate of Index of Shanghai Stock Exchange are come
The earning rate of analyst is calculated, the result is considered the situation of change of all market value of stock, calculated based on this situation of change
Earning rate more realistically reflect the professional skill of analyst.
The 3rd embodiment of the present invention provides a kind of data average growth rate computing device, as shown in figure 3, the device bag
Include：
Acquisition module 31, for obtaining the predicted time for assessing object to each target data；
Numerical value determining module 32, for obtain respectively numerical value of each the described target data in the predicted time and
Numerical value of each the described target data in the scheduled time, wherein the scheduled time is after the predicted time；
Mean value calculation module 33, for according to the numerical value and institute of each target data in the predicted time
State the average growth rate that object institute prediction data is assessed described in numerical computations of each target data in the scheduled time.
The data average growth rate computing device provided according to embodiments of the present invention, obtains assess object to each mesh first
The predicted time of data is marked, it is then determined that numerical value of each target data in the predicted time and after during a period of time
Numerical value, after being verified to same a large amount of prediction data for providing of assessment object, the institute of the assessment object can be calculated
There is the average growth rate of prediction object, this data can reflect the confidence level for assessing object, it is possible to be used as target data
The important references index of user, so as to assist user to assess or determine the performance of data prediction side, and then assists user's logarithm
According to change make counte-rplan in advance.
Preferably, the device also includes：
Growth Rate Calculation module 34, for after numerical value determining module processing, respectively according to each described target
Numerical value of the data in the scheduled time and each mesh described in numerical computations of each the described target data in the predicted time
Mark the growth rate of data；
Fluctuation computing module 35, for after mean value calculation resume module completion, according to each described mesh
The growth rate and the average growth rate for marking data calculate the fluctuation predicted the outcome for assessing object.
Preferably, the fluctuation computing module calculates the fluctuation using following formula：
D=1/n ((X1-S)2+(X2-S)2+(X3-S)2+……+(Xn-S)2),
Wherein D is the fluctuation, and X1, X2, X3 ... Xn are the growth rate of each target data, and S is described flat
Equal growth rate, the quantity for the target data that n is predicted by the assessment object.
Aforesaid way respectively subtracts each other the growth rate of each target data and average growth rate, and result then is done into a square fortune
Calculate, finally with the summation of square operation result divided by the number of target data, ripple of the result as data prediction result will be obtained
Dynamic property.Above-mentioned preferred scheme considers the single difference predicted the outcome with average growth rate, has thus reflected the stabilization of prediction side
Property, it further have evaluated the performance of prediction side.
Preferably, the assessment object is to grind to report data publisher, the target data to grind count off according to involved to be described
The market value of stock, the predicted time are the issuing time for grinding count off evidence；
The acquisition module includes：
Grind report data acquisition module, for obtain it is multiple grind count off evidence, it is described grind count off according to include distributor information with
To the tendency prediction information and issuing time of the market value of stock；
Forward direction grinds count off according to screening module, for from it is the multiple grind count off and filter out the tendency prediction information in refer to
The trend shown grinds count off evidence for positive growth；
Publisher's screening module, the count off of grinding for being increased according to the distributor information from forward direction determines institute's commentary in
That estimates object publishing grinds count off evidence；
Information of forecasting acquisition module, for determining issuing time in from the count off of grinding of determination respectively.
The data average growth rate computing device provided according to embodiments of the present invention, obtains analyst to each stock first
The predicted time of market value, it is then determined that numerical value of each market value of stock in the predicted time and after during a period of time
Numerical value, the real growth rate of each stock can be calculated according to the numerical value at the two moment, by being provided to same analyst
It is a large amount of grind count off according to being verified after, the average growth rate for all market value of stock that the analyst analyzed can be calculated,
This data can reflect the confidence level of analyst, it is possible to as the important references index of investor, so as to assist investment
Person assesses or determined the professional skill of analyst, and then assists change of the investor to the market value of stock to make counte-rplan in advance.
Preferably, the device also includes：
Stock index average growth rate determining module, for according to stock index in the prediction of each target data
Between and the numerical computations during scheduled time described in stock index average growth rate；
Earning rate computing module, for after mean value calculation module completion processing, report data publication to be ground in calculating
Person predicts the difference of the average growth rate of stock and the average growth rate of the stock index, and report data publication is ground as described
The earning rate of person.
Above-mentioned preferred scheme binding analysis teacher predicts that the average growth rate of stock and the average growth rate of Index of Shanghai Stock Exchange are come
The earning rate of analyst is calculated, the result is considered the situation of change of all market value of stock, calculated based on this situation of change
Earning rate more realistically reflect the professional skill of analyst.
Obviously, above-described embodiment is only intended to clearly illustrate example, and the not restriction to embodiment.It is right
For those of ordinary skill in the art, can also make on the basis of the above description it is other it is various forms of change or
Change.There is no necessity and possibility to exhaust all the enbodiments.And the obvious change thus extended out or
Among changing still in the protection domain of the invention.
Claims (10)
1. a kind of data average growth rate computational methods, it is characterised in that including：
Obtain the predicted time for assessing object to each target data；
Numerical value and described each target data of each the described target data in the predicted time are obtained respectively predetermined
The numerical value during time, wherein the scheduled time is after the predicted time；
According to numerical value of each the described target data in the predicted time and each described target data in the scheduled time
When numerical computations described in assess the average growth rate of object institute prediction data.
2. according to the method described in claim 1, it is characterised in that obtain each described target data respectively described described
After the numerical value of numerical value and each described target data in scheduled time during predicted time, also include：
Respectively according to the numerical value of each target data in the scheduled time and each described target data in the prediction
Between when numerical computations described in each target data growth rate；
Numerical value and each described target data when each target data is in the predicted time described in the basis is pre-
After the average growth rate that object institute prediction data is assessed described in numerical computations when fixing time, also include：
Calculated according to the growth rate of each target data and the average growth rate and described assess predicting the outcome for object
Fluctuation.
3. method according to claim 2, it is characterised in that the growth rate of each target data described in the basis and institute
State average growth rate and calculate the fluctuation predicted the outcome for assessing object, including：
The fluctuation is calculated using following formula：
D=1/n ((X1-S)2+(X2-S)2+(X3-S)2+……+(Xn-S)2),
Wherein D is the fluctuation, and X1, X2, X3 ... Xn are the growth rate of each target data, and S is the average increasing
Long rate, the quantity for the target data that n is predicted by the assessment object.
4. the method according to any one of claim 1-3, it is characterised in that the assessment object is to grind report data publication
Person, the target data are that the count off market value of stock, the predicted time involved by of grinding is the hair for grinding count off evidence
The cloth time；
The predicted time that assessment object is obtained to each target data, including：
Obtain it is multiple grind count off evidence, it is described grind count off according to include distributor information and tendency prediction information to the market value of stock with
Issuing time；
From it is the multiple grind count off filtered out in trend that the tendency prediction information indicates for it is positive increase grind count off evidence；
The count off of grinding increased according to the distributor information from forward direction determines that the assessment object publishing grinds count off evidence in；
Respectively issuing time is determined from the count off of grinding of determination in.
5. method according to claim 4, it is characterised in that also include：
According to numerical value of the stock index in predicted time and the scheduled time of each target data
Calculate the average growth rate of the stock index；
Numerical value and each described target data when each target data is in the predicted time described in the basis is pre-
After the average growth rate that object institute prediction data is assessed described in numerical computations when fixing time, also include：
The difference for the average growth rate for grinding the average growth rate and stock index for reporting data publisher to predict stock is calculated,
The earning rate of data publisher is reported as described grind.
6. a kind of data average growth rate computing device, it is characterised in that including：
Acquisition module, for obtaining the predicted time for assessing object to each target data；
Numerical value determining module, for obtaining numerical value of each the described target data in the predicted time respectively and described each
Numerical value of the individual target data in the scheduled time, wherein the scheduled time is after the predicted time；
Mean value calculation module, for according to numerical value of each the described target data in the predicted time and it is described each
The average growth rate of object institute prediction data is assessed described in numerical computations of the target data in the scheduled time.
7. device according to claim 6, it is characterised in that also include：
Growth Rate Calculation module, for after numerical value determining module processing, being existed respectively according to each described target data
The numerical value during scheduled time and each target data described in numerical computations of each the described target data in the predicted time
Growth rate；
Fluctuation computing module, for after mean value calculation resume module completion, according to each described target data
Growth rate and the average growth rate calculate the fluctuation predicted the outcome for assessing object.
8. device according to claim 7, it is characterised in that the fluctuation computing module calculates the ripple using following formula
Dynamic property：
D=1/n ((X1-S)2+(X2-S)2+(X3-S)2+……+(Xn-S)2),
Wherein D is the fluctuation, and X1, X2, X3 ... Xn are the growth rate of each target data, and S is the average increasing
Long rate, the quantity for the target data that n is predicted by the assessment object.
9. the device according to any one of claim 6-8, it is characterised in that the assessment object is to grind report data publication
Person, the target data are that the count off market value of stock, the predicted time involved by of grinding is the hair for grinding count off evidence
The cloth time；
The acquisition module includes：
Grind report data acquisition module, for obtain it is multiple grind count off evidence, it is described to grind count off according to including distributor information and to stock
The tendency prediction information and issuing time of ticket market value；
Forward direction grinds count off according to screening module, for filtering out what the tendency prediction information was indicated from the multiple grind in count off evidence
Trend grinds count off evidence for positive growth；
Publisher's screening module, the count off of grinding for being increased according to the distributor information from forward direction determines the assessment pair in
Count off evidence is ground as issue；
Information of forecasting acquisition module, for determining issuing time in from the count off of grinding of determination respectively.
10. device according to claim 9, it is characterised in that also include：
Stock index average growth rate determining module, for according to stock index each target data predicted time and
The average growth rate of stock index described in numerical computations during the scheduled time；
Earning rate computing module, for after mean value calculation module completion processing, calculating, which is ground, to report data publisher institute
The difference of the average growth rate of stock and the average growth rate of the stock index is predicted, reports data publisher's as described grind
Earning rate.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109783716A (en) * | 2019-01-21 | 2019-05-21 | 贵州黔源电力股份有限公司 | A kind of power supply and demand data automatically extract and processing system and method |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109783716A (en) * | 2019-01-21 | 2019-05-21 | 贵州黔源电力股份有限公司 | A kind of power supply and demand data automatically extract and processing system and method |
| CN109783716B (en) * | 2019-01-21 | 2020-02-21 | 贵州黔源电力股份有限公司 | Automatic extraction and processing system and method for power supply and demand data |
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