CN108711069A - price estimation method and device, storage medium and electronic equipment - Google Patents
price estimation method and device, storage medium and electronic equipment Download PDFInfo
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Abstract
The invention discloses a kind of price estimation method and device, storage medium and electronic equipments, are related to field of computer technology.The price estimation method includes：Obtain the time series of historical price data；The statistical nature of historical price data in the application time section of the actual time window of the time series is obtained, and obtains the price estimation value of the actual time window according to the statistical nature of the actual time window；The application time section of the future time window of the time series is determined according to the statistical nature of the actual time window；The statistical nature of historical price data in the application time section of the future time window is obtained, and obtains the price estimation value of the future time window according to the statistical nature of the future time window.The disclosure more accurately can continuously be estimated price by way of dynamic adjustment time window application time section.
Description
Technical field
This disclosure relates to field of computer technology, in particular to a kind of price estimation method, price estimation device,
Storage medium and electronic equipment.
Background technology
With the fast development of computer technology, artificial intelligence can be applied in every profession and trade, for example, artificial intelligence
Processing thought can be applied in the transaction scene in financial market, wherein how preferably in time series dynamic prediction finance
Market product price fluctuation has become one of the technical problems that are urgent to solve.
Currently, generally use artificial experience and some fixed formula models are (for example, the Bu Laike-Si Keersi-Mo Dun phases
Weigh pricing model) come the case where predicting money market product price.On the one hand, artificial experience carries artificial subjective factor, subjective
Error in judgement often increase the cost of such as investment manager team；On the other hand, fixed formula model is generally too list
One, it can not preferably cope with various data variation.
In consideration of it, needing a kind of price estimation method, price estimation device, storage medium and electronic equipment.
It should be noted that information is only used for reinforcing the reason to the background of the disclosure disclosed in above-mentioned background technology part
Solution, therefore may include the information not constituted to the prior art known to persons of ordinary skill in the art.
Invention content
The disclosure is designed to provide a kind of price estimation method, price estimation device, storage medium and electronic equipment,
And then overcome the problems, such as at least to a certain extent since artifact and formula model are single and cause price estimation inaccurate
The problem of.
According to one aspect of the disclosure, a kind of price estimation method is provided, including：Obtain the time of historical price data
Sequence；The statistical nature of historical price data in the application time section of the actual time window of the time series is obtained, and
The price estimation value of the actual time window is obtained according to the statistical nature of the actual time window；According to it is described current when
Between window statistical nature determine the time series future time window application time section；Obtain the future time
The statistical nature of historical price data in the application time section of window, and obtained according to the statistical nature of the future time window
Take the price estimation value of the future time window.
Optionally, the price estimation value of the actual time window is obtained according to the statistical nature of the actual time window
Including：The sample of predetermined quantity is randomly generated according to probabilistic model and model parameter；Wherein, the model parameter includes described works as
The statistical nature of preceding time window；The valence of the actual time window is obtained according to the statistical nature of the sample of the predetermined quantity
Lattice discreet value.
Optionally, probabilistic model is normal distribution model.
Optionally, include according to the price estimation value of the statistical nature of the sample of predetermined quantity acquisition actual time window：
Calculate the average value of the sample of predetermined quantity；Obtain price estimation value of the calculated average value as actual time window.
Optionally, the future time window of the time series is determined according to the statistical nature of the actual time window
Application time section includes：If the statistical nature of the actual time window meets pre-provisioning request, by the future time
The application time section of window is determined as the first predetermined interval；If the statistical nature of the actual time window does not meet predetermined
It is required that the application time section of the future time window is then determined as the second predetermined interval.
Optionally, the time series of acquisition historical price data includes：Acquisition time week is determined according to price change degree
Phase；Historical price data are obtained by the acquisition time period；Successively the historical price data are arranged by generated time
Sequence, to form the time series of the historical price data.
Optionally, the price estimation method further includes：Denoising is carried out to the time series according to following formula：
Pi=(1-a) * Ppre+a*Picur
Wherein, PiFor the pretreatment price value at i moment in actual time window, PpreFor history valence in previous time window
The average value of lattice data, PicurFor the average value of historical price data in the actual time window, a is default weighted value.
According to one aspect of the disclosure, a kind of price estimation device is provided, including：Time series acquisition module, is used for
Obtain the time series of historical price data；Current discreet value acquisition module, the current time for obtaining the time series
The statistical nature of historical price data in the application time section of window, and obtained according to the statistical nature of the actual time window
Take the price estimation value of the actual time window；Future time interval determination module, for according to the actual time window
Statistical nature determine the time series future time window application time section；Next discreet value acquisition module is used
In the statistical nature of historical price data in the application time section for obtaining the future time window, and according to described lower a period of time
Between the statistical nature of window obtain the price estimation value of the future time window.
Optionally, the current discreet value acquisition module includes：Sample generation element, for according to probabilistic model and model
Stochastic parameter generates the sample of predetermined quantity；Wherein, the model parameter includes the statistical nature of the actual time window；When
Preceding discreet value acquiring unit, the valence for obtaining the actual time window according to the statistical nature of the sample of the predetermined quantity
Lattice discreet value.
Optionally, probabilistic model is normal distribution model.
Optionally, current discreet value acquiring unit obtains actual time window according to the statistical nature of the sample of predetermined quantity
Price estimation value include：Current discreet value acquiring unit calculates the average value of the sample of predetermined quantity, and obtains calculated
Price estimation value of the average value as actual time window.
Optionally, the future time interval determination module includes：First time interval determination unit, if for described
The statistical nature of actual time window meets pre-provisioning request, then the application time section of the future time window is determined as
One predetermined interval；Second time interval determination unit, if the statistical nature for the actual time window do not meet it is predetermined
It is required that the application time section of the future time window is then determined as the second predetermined interval.
Optionally, the time series acquisition module includes：Acquisition time period determination unit, for according to price change
Degree determines the acquisition time period；Historical price data capture unit, for obtaining historical price by the acquisition time period
Data；Time series forms unit, for being successively ranked up to the historical price data by generated time, described in formation
The time series of historical price data.
Optionally, the price estimation device further includes：Time series denoising module is used for according to following formula to described
Time series carries out denoising：
Pi=(1-a) * Ppre+a*Picur
Wherein, PiFor the pretreatment price value at i moment in actual time window, PpreFor history valence in previous time window
The average value of lattice data, PicurFor the average value of historical price data in the actual time window, a is default weighted value.
According to one aspect of the disclosure, a kind of storage medium is provided, computer program, the computer are stored thereon with
The price estimation method described in above-mentioned any one is realized when program is executed by processor.
According to one aspect of the disclosure, a kind of electronic equipment is provided, including：Processor；And memory, for storing
The executable instruction of the processor；Wherein, the processor is configured to above-mentioned to execute via the executable instruction is executed
Price estimation method described in any one.
In the technical solution that some embodiments of the present disclosure are provided, according to the system of actual time window in time series
It counts feature and obtains the price estimation value of actual time window, and future time window is determined according to the statistical nature of actual time window
The application time section of mouth obtains the price estimation value of future time window, a side according to the statistical nature of future time window
Face is determined the application time section of future time window based on the statistical nature of actual time window, is adjusted by this dynamic
The mode in time window application time section more accurately can continuously estimate price；On the other hand, compared to some
The scheme of artificial experience and set formula model is relied in technology, the disclosure, can while can cope with data various variation
To reduce due to economic loss caused by artificial subjective factor.
It should be understood that above general description and following detailed description is only exemplary and explanatory, not
The disclosure can be limited.
Description of the drawings
The drawings herein are incorporated into the specification and forms part of this specification, and shows the implementation for meeting the disclosure
Example, and together with specification for explaining the principles of this disclosure.It should be evident that the accompanying drawings in the following description is only the disclosure
Some embodiments for those of ordinary skill in the art without creative efforts, can also basis
These attached drawings obtain other attached drawings.In the accompanying drawings：
Fig. 1 diagrammatically illustrates the flow chart of price estimation method according to an exemplary embodiment of the present disclosure；
Fig. 2 diagrammatically illustrates the flow of the whole process of price estimation according to an exemplary embodiment of the present disclosure
Figure；
Fig. 3 diagrammatically illustrates the block diagram of price estimation device according to an exemplary embodiment of the present disclosure；
Fig. 4 diagrammatically illustrates the box of current discreet value acquisition module according to an exemplary embodiment of the present disclosure
Figure；
Fig. 5 diagrammatically illustrates the box of future time interval determination module according to an exemplary embodiment of the present disclosure
Figure；
Fig. 6 diagrammatically illustrates the block diagram of time series acquisition module according to an exemplary embodiment of the present disclosure；
Fig. 7 diagrammatically illustrates the block diagram of the price estimation device of the another exemplary embodiment according to the disclosure；
Fig. 8 shows the schematic diagram of storage medium according to an exemplary embodiment of the present disclosure；And
Fig. 9 diagrammatically illustrates the block diagram of electronic equipment according to an exemplary embodiment of the present disclosure.
Specific implementation mode
Example embodiment is described more fully with reference to the drawings.However, example embodiment can be with a variety of shapes
Formula is implemented, and is not understood as limited to example set forth herein；On the contrary, thesing embodiments are provided so that the disclosure will more
Fully and completely, and by the design of example embodiment comprehensively it is communicated to those skilled in the art.Described feature, knot
Structure or characteristic can be in any suitable manner incorporated in one or more embodiments.In the following description, it provides perhaps
More details fully understand embodiment of the present disclosure to provide.It will be appreciated, however, by one skilled in the art that can
It is omitted with technical solution of the disclosure one or more in the specific detail, or others side may be used
Method, constituent element, device, step etc..In other cases, be not shown in detail or describe known solution to avoid a presumptuous guest usurps the role of the host and
So that all aspects of this disclosure thicken.
In addition, attached drawing is only the schematic illustrations of the disclosure, it is not necessarily drawn to scale.Identical attached drawing mark in figure
Note indicates same or similar part, thus will omit repetition thereof.Some block diagrams shown in attached drawing are work(
Energy entity, not necessarily must be corresponding with physically or logically independent entity.Software form may be used to realize these work(
Energy entity, or these functional entitys are realized in one or more hardware modules or integrated circuit, or at heterogeneous networks and/or place
These functional entitys are realized in reason device device and/or microcontroller device.
Flow chart shown in attached drawing is merely illustrative, it is not necessary to including all steps.For example, the step of having
It can also decompose, and the step of having can merge or part merges, therefore the sequence actually executed is possible to according to actual conditions
Change.
It below will be by taking the estimating of money market product (for example, stock, futures etc.) price as an example to the exemplary of the disclosure
The price estimation method of embodiment illustrates.However, the price estimation method of the disclosure is also applied to other prices
The scene estimated, for example, being estimated to prices such as chemical products, casting raw material, some food, the disclosure is to described below
The application scenarios of price estimation method and device do not do specifically limited.
In addition, it is to be understood that the price estimation described in the disclosure refers to estimated to the tendency of price emphatically, with
Phase is instructed subsequently to the trading processing process of product.
Fig. 1 diagrammatically illustrates the flow chart of the price estimation method of the illustrative embodiments of the disclosure, with reference to figure 1,
The price estimation method of the disclosure may comprise steps of：
S10. the time series of historical price data is obtained.
Historical price data can be stored in the corresponding database of product.That is, with the variation in market, product
Price will appear fluctuation, and price data (e.g., daily, weekly etc.) can be stored to product and corresponded at regular intervals by server
Database in.However, it is understood that for the violent scene of price fluctuation, server can be by each real trade
The price data at time point is stored into database.
By taking the scene estimated to stock price as an example, historical price data can be away from nearest 1 year at present domestic stock
The daily stock price data of listed company's (for example, the last 100) in ticket market.Such as, if it is desired to the upward price trend of stock A
It is estimated, price data daily stock A in 1 year can be obtained over as historical price data.
For the process for the time series for obtaining historical price data, it is possible, firstly, to be adopted according to the determination of price change degree
Collect the time cycle, for example, being fluctuated in price change in smaller scene, only can daily or weekly acquire the valence of primary production
Lattice, and for frequent scene of merchandising, then it needs continually to carry out data acquisition, for example, each hour or minute to price number
According to being acquired；Next, can by the acquisition time period obtain historical price data, specifically, server can directly from
It stores and obtains historical price data in the database of historical price data, in addition, server can also be obtained by third-party platform
Take historical price data；Then, server can carry out historical price data by the time order and function that historical price data generate
Sequence, with the time series of history of forming price data.
S12. the statistics of historical price data in the application time section of the actual time window of the time series is obtained
Feature, and obtain according to the statistical nature of the actual time window price estimation value of the actual time window.
In the illustrative embodiments of the disclosure, time window can be a continuous time section.In addition, disclosure institute
The time window stated can be for example minimum chronomere with day, in such a case, it is possible to which the size of time window is set
It is set to 5 days.However, according to the difference of actual product price fluctuation situation, the size of time window can also be set as 3 days,
10 days etc..It is easily understood that time window can in a sliding manner described in traversal step S10 time series.For example,
When just starting the price data progress analyzing processing to the time series on December 31st, 1 day 1 January in 2017, currently
The time window corresponding date can be on January 5,1 day to 2017 January in 2017.
It is possible, firstly, to randomly generate the sample of predetermined quantity according to probabilistic model and model parameter.The exemplary reality of the disclosure
It can be normal distribution model to apply probabilistic model used by mode, however, should also be as being understood using the scheme of other models
To belong to the design of the present invention.In this case, statistical nature can include but is not limited to average value, standard variance, guard
Value etc..Wherein, average value can characterize the average level of historical price data, and standard variance can reflect historical price number
According to dispersion degree.In addition, conservative value can be a threshold value, in historical price data, 95% data are all higher than the threshold value,
As can be seen that conservative value characterizes the robustness of price estimation.In addition, the statistical nature such as variance, median can also answer
For in the disclosure.
Model parameter may include statistical nature of the current window in normal distribution model, in addition, model parameter may be used also
With including the sample size randomly generated, applicable section size, steady property coefficient etc., for example, sample size can be with ten thousand
For unit (such as 100,000), applicable section size is 10, and steady property coefficient is 0.95 (that is, 95% sample is more than guarantor
Keep value).In addition, these parameters can survey the empirical adjustment of effect progress according to returning for real data.
For the process for the sample for generating predetermined quantity, for example, Ping Junfenbu [ can be randomly generated;1,10]Between
5000 numbers, specifically, random function random () can be called, which can randomly generate the value between 0 and 1, then
These values, which are multiplied by 10, can generate Ping Junfenbu [1,10]Between 5000 numbers.
Next, the price estimation value of actual time window can be obtained according to the statistical nature of the sample of predetermined quantity.
Specifically, the average value of the sample extracted can be calculated, and using the average value as the price estimation value of actual time window.
Furthermore it is also possible to using the conservative value of sample as the price estimation value of actual time window.It is easily understood that production can be passed through
Product real price, which fluctuates situation and determines, is suitable for the corresponding statistical nature of actual time window price estimation value.
S14. answering for the future time window of the time series is determined according to the statistical nature of the actual time window
Use time interval.
According to some embodiments of the present disclosure, when can be determined according to the standard variance of price data in actual time window
Between sequence future time window application time section.
Specifically, may determine that whether the standard variance of actual time window meets pre-provisioning request, wherein pre-provisioning request can
To be, for example, 1/10 of standard variance not less than financial product price average value, so it is easy to understand that when meeting pre-provisioning request,
Then illustrate that price data fluctuation is larger.
On the one hand, if the standard variance of actual time window meets pre-provisioning request, by the application of future time window
Time interval is determined as the first predetermined interval.It, can be by first if actual time window to be denoted as to the time interval of t1 to tw
Predetermined interval is determined as such as t1+5 to tw+5.
On the other hand, if the standard variance of actual time window does not meet pre-provisioning request, illustrate that price data fluctuates
Not acutely, in such a case, it is possible to which the application time section of future time window is determined as the second predetermined interval.Wherein,
Second predetermined interval can be, for example, t1+10 to tw+10.
It should be understood that "+10 " are only exemplary description, root in "+5 " and the second predetermined interval in the first predetermined interval
According to actual conditions, the sliding step of time window can change.However, when the statistical nature dependent on actual time window determines
Between window sliding step scheme should all belong to the present invention design.
S16. the statistical nature of historical price data in the application time section of the future time window is obtained, and according to
The statistical nature of the future time window obtains the price estimation value of the future time window.
It obtains the statistical nature of historical price data in the application time section of future time window and obtains future time
The process of the price estimation value of window and historical price data in the application time section for obtaining actual time window in step S12
Statistical nature and obtain future time window price estimation value process it is similar, details are not described herein.
It should be understood that the dynamic with time window is slided, future time window will be used as actual time window.
In addition, in order to preferably be estimated to product price, the price estimation method of the disclosure can also include clock synchronization
Between sequence carry out denoising.Specifically, denoising can be carried out to time series according to following formula：
Pi=(1-a) * Ppre+a*Picur
Wherein, PiFor the pretreatment price value at i moment in actual time window, PpreFor history valence in previous time window
The average value of lattice data, PicurFor the average value of historical price data in actual time window, a is default weighted value and a ∈ [0,
1]。
The whole process of the price estimation method of the illustrative embodiments of the disclosure is said below with reference to Fig. 2
It is bright.
With reference to figure 2, in step s 201, server can obtain the time series of historical price data；In step S203
In, server can determine the application time section of actual time window；In step S205, when server can calculate current
Between window price data average value as valuation, in addition, server can also calculate the price data of actual time window
Conservative value as valuation；In step S207, server can calculate the standard variance of the price data of actual time window；
In step S209, server can be according to when calculated standard variance determines the application of future time window in step S207
Between section；In step S211, server can be using future time window as actual time window, and step jumps to step
Rapid S203.
In the price estimation method that some embodiments of the present disclosure are provided, according to actual time window in time series
Statistical nature obtain the price estimation value of actual time window, and according to the statistical nature of actual time window determine it is lower for the moment
Between window application time section, according to the statistical nature of future time window obtain future time window price estimation value,
On the one hand, the application time section that future time window is determined based on the statistical nature of actual time window, passes through this dynamic
The mode in adjustment time window application time section, more accurately can continuously estimate price；On the other hand, compared to
The scheme of artificial experience and set formula model is relied in some technologies, the disclosure can cope with the same of the various variation of data
When, it is possible to reduce due to economic loss caused by artificial subjective factor.
It should be noted that although describing each step of method in the disclosure with particular order in the accompanying drawings, this is simultaneously
Undesired or hint must execute these steps according to the particular order, or have to carry out the step ability shown in whole
Realize desired result.Additional or alternative, it is convenient to omit multiple steps are merged into a step and executed by certain steps,
And/or a step is decomposed into execution of multiple steps etc..
Further, a kind of price estimation device is additionally provided in this example embodiment.
Fig. 3 diagrammatically illustrates the block diagram of the price estimation device of the illustrative embodiments of the disclosure.With reference to figure 3,
Price estimation device 3 according to an exemplary embodiment of the present disclosure may include time series acquisition module 31, currently estimate
It is worth acquisition module 33, future time interval determination module 35 and next discreet value acquisition module 37.
Specifically, time series acquisition module 31 can be used for obtaining the time series of historical price data；Currently estimate
Value acquisition module 33 can be used for obtaining historical price number in the application time section of the actual time window of the time series
According to statistical nature, and obtain according to the statistical nature of the actual time window price estimation of the actual time window
Value；Future time interval determination module 35 can be used for determining the time sequence according to the statistical nature of the actual time window
The application time section of the future time window of row；Next discreet value acquisition module 37 can be used for obtaining the future time window
The statistical nature of historical price data in the application time section of mouth, and obtained according to the statistical nature of the future time window
The price estimation value of the future time window.
In the price estimation device of the illustrative embodiments of the disclosure, on the one hand, the system based on actual time window
Meter feature determines the application time section of future time window, passes through the side in this dynamic adjustment time window application time section
Formula more accurately can continuously estimate price；On the other hand, compared to dependence artificial experience and fixation in some technologies
The scheme of formula model, the disclosure is while can cope with data various variation, it is possible to reduce due to artificial subjective factor
Caused by economic loss.
According to an exemplary embodiment of the present disclosure, with reference to figure 4, current discreet value acquisition module 33 may include that sample generates
Unit 401 and current discreet value acquiring unit 403.
Specifically, sample generation element 401 can be used for randomly generating predetermined quantity according to probabilistic model and model parameter
Sample；Wherein, the model parameter includes the statistical nature of the actual time window；Current discreet value acquiring unit 403
It can be used for obtaining the price estimation value of the actual time window according to the statistical nature of the sample of the predetermined quantity.
According to an exemplary embodiment of the present disclosure, probabilistic model is normal distribution model.
According to an exemplary embodiment of the present disclosure, current discreet value acquiring unit 403 is according to the system of the sample of predetermined quantity
Meter feature obtain actual time window price estimation value include：Current discreet value acquiring unit 403 calculates the sample of predetermined quantity
This average value, and obtain price estimation value of the calculated average value as actual time window.
According to an exemplary embodiment of the present disclosure, with reference to figure 5, when future time interval determination module 35 may include first
Between interval determination unit 501 and the second time interval determination unit 503.
If specifically, first time interval determination unit 501 can be used for the statistical nature of the actual time window
Meet pre-provisioning request, then the application time section of the future time window is determined as the first predetermined interval；Second time zone
Determination unit 503 can be used for the statistical nature of the actual time window and not meet pre-provisioning request between if, under described
The application time section of one time window is determined as the second predetermined interval.
The application time section of different future time windows, Ke Yigeng are determined by current different price fluctuation situation
Accurately price is continuously estimated.
According to an exemplary embodiment of the present disclosure, with reference to figure 6, time series acquisition module 31 may include acquisition time week
Phase determination unit 601, historical price data capture unit 603 and time series form unit 605.
Specifically, acquisition time period determination unit 601 can be used for determining acquisition time week according to price change degree
Phase；Historical price data capture unit 603 can be used for obtaining historical price data by the acquisition time period；Time series
It forms unit 605 to can be used for successively being ranked up the historical price data by generated time, to form the history valence
The time series of lattice data.
According to an exemplary embodiment of the present disclosure, with reference to figure 7, price estimation device 7 is removed compared to price estimation device 3
Including time series acquisition module 31, current discreet value acquisition module 33, future time interval determination module 35 and next estimate
It is worth outside acquisition module 37, can also includes time series denoising module 71.
Specifically, time series denoising module 71 can be used for carrying out at denoising the time series according to following formula
Reason：
Pi=(1-a) * Ppre+a*Picur
Wherein, PiFor the pretreatment price value at i moment in actual time window, PpreFor history valence in previous time window
The average value of lattice data, PicurFor the average value of historical price data in the actual time window, a is default weighted value.
Since each function module of the program analysis of running performance device of embodiment of the present invention is invented with the above method
It is identical in embodiment, therefore details are not described herein.
In an exemplary embodiment of the disclosure, a kind of computer readable storage medium is additionally provided, energy is stored thereon with
Enough realize the program product of this specification above method.In some possible embodiments, various aspects of the invention may be used also
In the form of being embodied as a kind of program product comprising program code, when described program product is run on the terminal device, institute
State program code for make the terminal device execute described in above-mentioned " illustrative methods " part of this specification according to this hair
The step of bright various illustrative embodiments.
Refering to what is shown in Fig. 8, describing the program product for realizing the above method according to the embodiment of the present invention
800, portable compact disc read only memory (CD-ROM) may be used and include program code, and can in terminal device,
Such as it is run on PC.However, the program product of the present invention is without being limited thereto, in this document, readable storage medium storing program for executing can be with
To be any include or the tangible medium of storage program, the program can be commanded execution system, device either device use or
It is in connection.
The arbitrary combination of one or more readable mediums may be used in described program product.Readable medium can be readable letter
Number medium or readable storage medium storing program for executing.Readable storage medium storing program for executing for example can be but be not limited to electricity, magnetic, optical, electromagnetic, infrared ray or
System, device or the device of semiconductor, or the arbitrary above combination.The more specific example of readable storage medium storing program for executing is (non exhaustive
List) include：It is electrical connection, portable disc, hard disk, random access memory (RAM) with one or more conducting wires, read-only
Memory (ROM), erasable programmable read only memory (EPROM or flash memory), optical fiber, portable compact disc read only memory
(CD-ROM), light storage device, magnetic memory device or above-mentioned any appropriate combination.
Computer-readable signal media may include in a base band or as the data-signal that a carrier wave part is propagated,
In carry readable program code.The data-signal of this propagation may be used diversified forms, including but not limited to electromagnetic signal,
Optical signal or above-mentioned any appropriate combination.Readable signal medium can also be any readable Jie other than readable storage medium storing program for executing
Matter, which can send, propagate either transmission for used by instruction execution system, device or device or and its
The program of combined use.
The program code for including on readable medium can transmit with any suitable medium, including but not limited to wirelessly, have
Line, optical cable, RF etc. or above-mentioned any appropriate combination.
It can be write with any combination of one or more programming languages for executing the program that operates of the present invention
Code, described program design language include object oriented program language-Java, C++ etc., further include conventional
Procedural programming language-such as " C " language or similar programming language.Program code can be fully in user
It executes on computing device, partly execute on a user device, being executed as an independent software package, partly in user's calculating
Upper side point is executed or is executed in remote computing device or server completely on a remote computing.It is being related to far
In the situation of journey computing device, remote computing device can pass through the network of any kind, including LAN (LAN) or wide area network
(WAN), it is connected to user calculating equipment, or, it may be connected to external computing device (such as utilize ISP
To be connected by internet).
In an exemplary embodiment of the disclosure, a kind of electronic equipment that can realize the above method is additionally provided.
Person of ordinary skill in the field it is understood that various aspects of the invention can be implemented as system, method or
Program product.Therefore, various aspects of the invention can be embodied in the following forms, i.e.,：It is complete hardware embodiment, complete
The embodiment combined in terms of full Software Implementation (including firmware, microcode etc.) or hardware and software, can unite here
Referred to as circuit, " module " or " system ".
The electronic equipment 900 of this embodiment according to the present invention is described referring to Fig. 9.The electronics that Fig. 9 is shown
Equipment 900 is only an example, should not bring any restrictions to the function and use scope of the embodiment of the present invention.
As shown in figure 9, electronic equipment 900 is showed in the form of universal computing device.The component of electronic equipment 900 can wrap
It includes but is not limited to：Above-mentioned at least one processing unit 910, above-mentioned at least one storage unit 920, connection different system component
The bus 930 of (including storage unit 920 and processing unit 910), display unit 940.
Wherein, the storage unit has program stored therein code, and said program code can be held by the processing unit 910
Row so that the processing unit 910 executes various according to the present invention described in above-mentioned " illustrative methods " part of this specification
The step of illustrative embodiments.For example, the processing unit 910 can execute step S10 as shown in fig. 1：Acquisition is gone through
The time series of history price data；Step S12：It obtains and is gone through in the application time section of the actual time window of the time series
The statistical nature of history price data, and obtain according to the statistical nature of the actual time window valence of the actual time window
Lattice discreet value；Step S14：The future time window of the time series is determined according to the statistical nature of the actual time window
Application time section；And step S16：Obtain historical price data in the application time section of the future time window
Statistical nature, and obtain according to the statistical nature of the future time window price estimation value of the future time window.
Storage unit 920 may include the readable medium of volatile memory cell form, such as Random Access Storage Unit
(RAM) 9201 and/or cache memory unit 9202, it can further include read-only memory unit (ROM) 9203.
Storage unit 920 can also include program/utility with one group of (at least one) program module 9205
9204, such program module 9205 includes but not limited to：Operating system, one or more application program, other program moulds
Block and program data may include the realization of network environment in each or certain combination in these examples.
Bus 930 can be to indicate one or more in a few class bus structures, including storage unit bus or storage
Cell controller, peripheral bus, graphics acceleration port, processing unit use the arbitrary bus structures in a variety of bus structures
Local bus.
Electronic equipment 900 can also be with one or more external equipments 1000 (such as keyboard, sensing equipment, bluetooth equipment
Deng) communication, can also be enabled a user to one or more equipment interact with the electronic equipment 900 communicate, and/or with make
Any equipment that the electronic equipment 900 can be communicated with one or more of the other computing device (such as router, modulation /demodulation
Device etc.) communication.This communication can be carried out by input/output (I/O) interface 950.Also, electronic equipment 900 can be with
By network adapter 960 and one or more network (such as LAN (LAN), wide area network (WAN) and/or public network,
Such as internet) communication.As shown, network adapter 960 is communicated by bus 930 with other modules of electronic equipment 900.
It should be understood that although not shown in the drawings, other hardware and/or software module can not used in conjunction with electronic equipment 900, including but not
It is limited to：Microcode, device driver, redundant processing unit, external disk drive array, RAID system, tape drive and
Data backup storage system etc..
Through the above description of the embodiments, those skilled in the art is it can be readily appreciated that example described herein is implemented
Mode can also be realized by software realization in such a way that software is in conjunction with necessary hardware.Therefore, according to the disclosure
The technical solution of embodiment can be expressed in the form of software products, the software product can be stored in one it is non-volatile
Property storage medium (can be CD-ROM, USB flash disk, mobile hard disk etc.) in or network on, including some instructions are so that a calculating
Equipment (can be personal computer, server, terminal installation or network equipment etc.) is executed according to disclosure embodiment
Method.
In addition, above-mentioned attached drawing is only the schematic theory of the processing included by method according to an exemplary embodiment of the present invention
It is bright, rather than limit purpose.It can be readily appreciated that the time that above-mentioned processing shown in the drawings did not indicated or limited these processing is suitable
Sequence.In addition, being also easy to understand, these processing for example can be executed either synchronously or asynchronously in multiple modules.
It should be noted that although being referred to several modules or list for acting the equipment executed in above-detailed
Member, but this division is not enforceable.In fact, according to embodiment of the present disclosure, it is above-described two or more
The feature and function of module either unit can embody in a module or unit.Conversely, an above-described mould
Either the feature and function of unit can be further divided into and embodied by multiple modules or unit block.
Those skilled in the art after considering the specification and implementing the invention disclosed here, will readily occur to its of the disclosure
His embodiment.This application is intended to cover any variations, uses, or adaptations of the disclosure, these modifications, purposes or
Adaptive change follow the general principles of this disclosure and include the undocumented common knowledge in the art of the disclosure or
Conventional techniques.The description and examples are only to be considered as illustrative, and the true scope and spirit of the disclosure are by claim
It points out.
It should be understood that the present disclosure is not limited to the precise structures that have been described above and shown in the drawings, and
And various modifications and changes may be made without departing from the scope thereof.The scope of the present disclosure is only limited by the attached claims.
Claims (10)
1. a kind of price estimation method, which is characterized in that including：
Obtain the time series of historical price data；
Obtain the statistical nature of historical price data in the application time section of the actual time window of the time series, and root
The price estimation value of the actual time window is obtained according to the statistical nature of the actual time window；
The application time area of the future time window of the time series is determined according to the statistical nature of the actual time window
Between；
The statistical nature of historical price data in the application time section of the future time window is obtained, and according to described next
The statistical nature of time window obtains the price estimation value of the future time window.
2. price estimation method according to claim 1, which is characterized in that special according to the statistics of the actual time window
The price estimation value that sign obtains the actual time window includes：
The sample of predetermined quantity is randomly generated according to probabilistic model and model parameter；Wherein, the model parameter includes described works as
The statistical nature of preceding time window；
The price estimation value of the actual time window is obtained according to the statistical nature of the sample of the predetermined quantity.
3. price estimation method according to claim 2, which is characterized in that the probabilistic model is normal distribution model.
4. price estimation method according to claim 2 or 3, which is characterized in that according to the sample of the predetermined quantity
The price estimation value that statistical nature obtains the actual time window includes：
Calculate the average value of the sample of the predetermined quantity；
Obtain price estimation value of the calculated average value as the actual time window.
5. price estimation method according to claim 1, which is characterized in that special according to the statistics of the actual time window
Sign determines that the application time section of the future time window of the time series includes：
If the statistical nature of the actual time window meets pre-provisioning request, by the application time of the future time window
Section is determined as the first predetermined interval；
If the statistical nature of the actual time window does not meet pre-provisioning request, when by the application of the future time window
Between section be determined as the second predetermined interval.
6. price estimation method according to claim 1, which is characterized in that obtain the time series packet of historical price data
It includes：
The acquisition time period is determined according to price change degree；
Historical price data are obtained by the acquisition time period；
Successively the historical price data are ranked up by generated time, to form the time sequence of the historical price data
Row.
7. price estimation method according to claim 1, which is characterized in that the price estimation method further includes：
Denoising is carried out to the time series according to following formula：
Pi=(1-a) * Ppre+a*Picur
Wherein, PiFor the pretreatment price value at i moment in actual time window, PpreFor historical price data in previous time window
Average value, PicurFor the average value of historical price data in the actual time window, a is default weighted value.
8. a kind of price estimation device, which is characterized in that including：
Time series acquisition module, the time series for obtaining historical price data；
Current discreet value acquisition module, history in the application time section of the actual time window for obtaining the time series
The statistical nature of price data, and obtain according to the statistical nature of the actual time window price of the actual time window
Discreet value；
Future time interval determination module, for determining the time series according to the statistical nature of the actual time window
The application time section of future time window；
Next discreet value acquisition module, historical price data in the application time section for obtaining the future time window
Statistical nature, and obtain according to the statistical nature of the future time window price estimation value of the future time window.
9. a kind of storage medium, is stored thereon with computer program, which is characterized in that the computer program is executed by processor
Price estimation method described in any one of Shi Shixian claims 1 to 7.
10. a kind of electronic equipment, which is characterized in that including：
Processor；And
Memory, the executable instruction for storing the processor；
Wherein, the processor is configured to come described in any one of perform claim requirement 1 to 7 via the execution executable instruction
Price estimation method.
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| CN201810415842.1A CN108711069A (en) | 2018-05-03 | 2018-05-03 | price estimation method and device, storage medium and electronic equipment |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN110599258A (en) * | 2019-09-20 | 2019-12-20 | 中国银行股份有限公司 | Method and device for prompting influence information of regular events on product price change |
| CN111652641A (en) * | 2020-05-29 | 2020-09-11 | 泰康保险集团股份有限公司 | Data processing method, device, equipment and computer readable storage medium |
| CN111815346A (en) * | 2020-04-10 | 2020-10-23 | 北京嘀嘀无限科技发展有限公司 | Method and device for estimating settlement data, storage medium and electronic equipment |
| CN112035252A (en) * | 2020-08-26 | 2020-12-04 | 中国建设银行股份有限公司 | Task processing method, device, equipment and medium |
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| CN113900784A (en) * | 2021-10-09 | 2022-01-07 | 北京房江湖科技有限公司 | Method and device for determining task baseline time, electronic equipment and storage medium |
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| CN110599258A (en) * | 2019-09-20 | 2019-12-20 | 中国银行股份有限公司 | Method and device for prompting influence information of regular events on product price change |
| CN111815346A (en) * | 2020-04-10 | 2020-10-23 | 北京嘀嘀无限科技发展有限公司 | Method and device for estimating settlement data, storage medium and electronic equipment |
| CN111652641A (en) * | 2020-05-29 | 2020-09-11 | 泰康保险集团股份有限公司 | Data processing method, device, equipment and computer readable storage medium |
| CN111652641B (en) * | 2020-05-29 | 2023-07-25 | 泰康保险集团股份有限公司 | Data processing method, device, equipment and computer readable storage medium |
| CN112035252A (en) * | 2020-08-26 | 2020-12-04 | 中国建设银行股份有限公司 | Task processing method, device, equipment and medium |
| CN112035252B (en) * | 2020-08-26 | 2024-08-27 | 中国建设银行股份有限公司 | Task processing method, device, equipment and medium |
| CN112215643A (en) * | 2020-10-12 | 2021-01-12 | 上海酷量信息技术有限公司 | Preloading system and method based on historical advertising price |
| CN112612996A (en) * | 2020-12-25 | 2021-04-06 | 北京知因智慧科技有限公司 | Sampling time granularity selection method and device, electronic equipment and storage medium |
| CN112612996B (en) * | 2020-12-25 | 2024-06-28 | 北京知因智慧科技有限公司 | Sampling time granularity selection method and device, electronic equipment and storage medium |
| CN113900784A (en) * | 2021-10-09 | 2022-01-07 | 北京房江湖科技有限公司 | Method and device for determining task baseline time, electronic equipment and storage medium |
| CN115131120A (en) * | 2022-09-02 | 2022-09-30 | 合肥本源量子计算科技有限责任公司 | Quantum option estimation method based on least square method and related device |
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Application publication date: 20181026 |