CN113313525A - Price prediction method of international passenger ticket, related device and computer storage medium - Google Patents
Price prediction method of international passenger ticket, related device and computer storage medium Download PDFInfo
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Abstract
The application provides a price prediction method of an international passenger ticket, a related device and a computer storage medium, wherein the method comprises the following steps: determining data to be predicted according to historical freight rate information; updating the holiday list, and removing the unified price adjustment data of the ordinary date in the data to be predicted according to the holiday list to obtain first prediction data; predicting according to the first prediction data to obtain a prediction result of the common date; acquiring freight rate data corresponding to dates in the holiday list from the historical freight rate data according to the holiday list to generate a prediction two-dimensional array; carrying out unified price adjustment processing on each date in the predicted two-dimensional array to obtain at least one special freight rate date; predicting each special holiday period to obtain a holiday prediction result; and integrating the prediction result of the common date and the prediction results of the festivals and holidays, and finally outputting the price prediction result of the international passenger ticket on the current day. Therefore, the aim of accurately obtaining the price prediction result of the international passenger ticket on the day is fulfilled.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to a method for predicting a price of an international ticket, a related apparatus, and a computer storage medium.
Background
When a civil aviation passenger buys an air ticket of an international airline, whether a target airline has a low price or a low price in a future period of time or not has a great influence on a purchasing decision and a trip plan of the civil aviation passenger.
At present, in order to give reasonable decision to passengers according to airline fares in a future period, low price prediction in the future period becomes a necessary decision condition. The fare data of international airlines are generally analyzed, the fares of the international airlines are sparse, and business operations and special dates or events cause special rules of fare changes.
However, most of the existing airline low-price prediction methods are based on the traditional time series or machine learning, and the methods are based on historical fare characteristics and distribution, so that the special rules of fare changes caused by business operations and special dates or events cannot be identified, and the result of low-price prediction is inaccurate.
Disclosure of Invention
In view of the above, the present application provides a method, a related apparatus, and a computer storage medium for predicting a price of an international ticket, which are used to accurately obtain a price prediction result of the international ticket of this day.
The first aspect of the present application provides a method for predicting the price of an international passenger ticket, comprising:
determining data to be predicted according to historical freight rate information;
updating a holiday list, and removing unified price adjustment data of an ordinary date in the data to be predicted according to the holiday list to obtain first prediction data; wherein the ordinary date is a date not in the holiday list;
extracting a two-dimensional array in the first prediction data, and predicting the two-dimensional array according to the freight rate time sequence and the historical freight rate time sequence to obtain a prediction result of a common date;
acquiring freight rate data corresponding to dates in the holiday list from historical freight rate data according to the holiday list, forming a time sequence, and generating a prediction two-dimensional array;
carrying out unified price adjustment processing on each date in the predicted two-dimensional array, and determining at least one special freight rate date;
predicting the special holiday dates according to each special freight rate date to obtain a prediction result of the holidays;
and integrating the prediction result of the common date and the prediction result of the holiday, and outputting the price prediction result of the international passenger ticket on the current day.
Optionally, the determining data to be predicted according to the historical freight rate information includes:
judging whether the currently executed price prediction operation is the first execution price prediction operation;
if the currently executed price prediction operation is judged to be the first-time execution price prediction operation, reading the full-amount historical freight rate information and constructing a basic data file;
performing interpolation fitting on the basic data file to construct data to be predicted;
if the currently executed price prediction operation is judged not to be the first time price prediction operation, adding the currently newly added date data information, dividing the historical and future departure date freight rate information, and updating the basic data file;
and carrying out interpolation fitting on the basic data files, and combining historical freight rate data to construct data to be predicted.
Optionally, the updating the holiday list includes:
reading in historical data information when price prediction operation is executed last time;
reading a holiday list in the historical data information;
adding data which is in line but not covered by data in the holiday list in the historical data information to a current holiday list according to the data to be predicted.
Optionally, the method for predicting the price of the international passenger ticket further includes:
acquiring the total amount of data to be predicted, and reading data information in a current holiday list;
and when the freight rate data jump on the departure date, adjusting the data information in the current holiday list.
Optionally, the removing, according to the holiday list, the uniform price adjustment data of the ordinary date in the data to be predicted to obtain first prediction data includes:
determining the interval of the search date according to the quotation date;
performing non-numeric supplementation on data which does not exist in the search date interval;
forming a two-dimensional matrix in a mode of ascending the departure date and descending the search date;
constructing a two-dimensional array of the search date according to the two-dimensional matrix;
inquiring the freight rate data corresponding to each search date in the two-dimensional array of the search dates;
aiming at each search date, judging whether unified price adjustment exists on the search date according to the freight rate data corresponding to the search date;
and if the search date is judged to have the unified price adjustment, removing the search date and the freight rate data corresponding to the search date from the two-dimensional array of the search date to obtain first prediction data.
Optionally, after integrating the prediction result of the common date and the prediction result of the holiday and outputting the price prediction result of the international passenger ticket on the current day, the method further includes:
receiving a query request of a client; wherein the query request includes at least: a departure location, a destination, and a departure date;
and screening at least one piece of ticket information corresponding to the query request of the client in the price prediction result of the international ticket in the current day, and displaying each piece of ticket information to the client.
A second aspect of the present application provides an international ticket price predicting apparatus, comprising:
the first determining unit is used for determining data to be predicted according to the historical freight rate information;
the updating unit is used for updating the holiday list;
the first prediction unit is used for removing the unified price adjusting data of the ordinary date in the data to be predicted according to the holiday list to obtain first prediction data; wherein the ordinary date is a date not in the holiday list;
the extraction unit is used for extracting the two-dimensional array in the first prediction data, predicting the two-dimensional array according to the freight rate time sequence and the historical freight rate time sequence, and obtaining a prediction result of a common date;
the generating unit is used for acquiring freight rate data corresponding to dates in the holiday list from historical freight rate data according to the holiday list, forming a time sequence and generating a prediction two-dimensional array;
the second determining unit is used for carrying out unified price adjustment processing on each date in the prediction two-dimensional array and determining at least one special freight rate date;
the second prediction unit is used for predicting the special holiday dates according to each special freight rate date to obtain a prediction result of the holidays;
and the integration unit is used for integrating the prediction result of the common date and the prediction result of the holiday and outputting the price prediction result of the international passenger ticket on the current day.
Optionally, the first determining unit includes:
a first judgment unit configured to judge whether or not a currently executed price prediction operation is a first execution price prediction operation;
the first reading unit is used for reading the full-amount historical freight rate information and constructing a basic data file if the first judging unit judges that the currently executed price prediction operation is the first-time executed price prediction operation;
the construction unit is used for carrying out interpolation fitting on the basic data file and constructing data to be predicted;
a first adding unit, configured to add current newly added date data information, divide history and future departure date freight rate information, and update the basic data file, if the first determining unit determines that the currently executed price prediction operation is not the first-time executed price prediction operation;
the construction unit is also used for carrying out interpolation fitting on the basic data files, combining historical freight rate data and constructing data to be predicted.
Optionally, the update unit includes:
a reading unit for reading in history data information when the price prediction operation was executed last time;
the second reading unit is used for reading a holiday list in the historical data information;
and the second adding unit is used for adding data which is in a row but is not covered by the data in the holiday list in the historical data information into the current holiday list according to the data to be predicted.
Optionally, the price predicting device for international passenger tickets further includes:
the acquiring unit is used for acquiring the total amount of data to be predicted and reading data information in the current holiday list;
and the adjusting unit is used for adjusting the data information in the current holiday list when the jumping of the freight rate data occurs on the departure date.
Optionally, the first prediction unit includes:
a third determination unit configured to determine an interval of the search date according to the quoted date;
a supplement unit configured to supplement data that does not exist in the search date section with a non-data supplement;
the composition unit is used for composing a two-dimensional matrix in a mode of ascending the departure date and descending the search date;
the construction unit is used for constructing a two-dimensional array of the search date according to the two-dimensional matrix;
the query unit is used for querying the freight rate data corresponding to each search date in the two-dimensional array of the search dates;
the second judgment unit is used for judging whether the search dates have unified price adjustment or not according to the freight rate data corresponding to the search dates aiming at each search date;
and the removing unit is used for removing the search date and the freight rate data corresponding to the search date from the two-dimensional array of the search date to obtain first prediction data if the search date has unified price adjustment according to the judgment of the second judging unit.
Optionally, the price predicting device for international passenger tickets further includes:
the receiving unit is used for receiving a query request of a client; wherein the query request includes at least: a departure location, a destination, and a departure date;
and the display unit is used for screening at least one piece of ticket information corresponding to the inquiry request of the client in the price prediction result of the international ticket at the current day and displaying each piece of ticket information to the client.
In view of the above, the present application provides a method for predicting the price of an international ticket, a related device and a computer storage medium, wherein the method for predicting the price of an international ticket includes: firstly, determining data to be predicted according to historical freight rate information; then, updating a holiday list, and removing the unified price adjustment data of the ordinary date in the data to be predicted according to the holiday list to obtain first prediction data; wherein the ordinary date is a date not in the holiday list; then, extracting a two-dimensional array in the first prediction data, and predicting the two-dimensional array according to the freight rate time sequence and the historical freight rate time sequence to obtain a prediction result of a common date; acquiring freight rate data corresponding to dates in the holiday list from historical freight rate data according to the holiday list, forming a time sequence, and generating a prediction two-dimensional array; carrying out unified price adjustment processing on each date in the predicted two-dimensional array, and determining at least one special freight rate date; predicting the special holiday dates according to each special freight rate date to obtain a prediction result of the holiday dates; and finally, integrating the prediction result of the common date and the prediction result of the holidays, and outputting the price prediction result of the international passenger ticket on the current day. Therefore, the aim of accurately obtaining the price prediction result of the international passenger ticket on the day is fulfilled.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
FIG. 1 is a flowchart illustrating a method for predicting the price of an international ticket according to an embodiment of the present disclosure;
FIG. 2 is a flow chart of a manner of constructing data to be predicted according to another embodiment of the present application;
FIG. 3 is a flowchart of an update holiday list according to another embodiment of the present application;
FIG. 4 is a flowchart of a method for removing the current day's consolidated price data from the data to be forecasted according to another embodiment of the present application;
FIG. 5 is a flow chart of a method for price forecasting of international tickets according to another embodiment of the present application;
FIG. 6 is a schematic diagram of an apparatus for price forecasting of international tickets according to another embodiment of the present application;
fig. 7 is a schematic diagram of an electronic device implementing a method for predicting international tickets according to another embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The term "include" and variations thereof as used herein are open-ended, i.e., "including but not limited to". The term "based on" is "based, at least in part, on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Relevant definitions for other terms will be given in the following description.
It should be noted that the terms "first", "second", and the like in the present application are only used for distinguishing different devices, modules or units, and are not used for limiting the order or interdependence relationship of the functions performed by the devices, modules or units.
It is noted that references to "a", "an", and "the" modifications in this application are intended to be illustrative rather than limiting, and that those skilled in the art will recognize that reference to "one or more" unless the context clearly dictates otherwise.
The embodiment of the application provides a method for predicting the price of an international passenger ticket, which specifically comprises the following steps as shown in fig. 1:
and S101, determining data to be predicted according to the historical freight rate information.
It should be noted that the data to be predicted is determined again every day according to the historical freight rate information.
Optionally, in another embodiment of the present application, an implementation manner of step S101, as shown in fig. 2, includes:
s201, judging whether the currently executed price prediction operation is the first-time execution price prediction operation.
Specifically, if it is determined that the currently executed price prediction operation is the first price prediction operation, step S202 is executed; if it is determined that the currently executed price prediction operation is not the first executed price prediction operation, step S204 is executed.
S202, reading the total historical freight rate information and constructing a basic data file.
And S203, carrying out interpolation fitting on the basic data file to construct data to be predicted.
It should be noted that, the interpolation fitting method for the basic data file may be, but is not limited to, a method of edge deletion + median deletion, which is not limited herein.
And S204, adding the current newly added date data information, dividing the historical and future departure date freight rate information, and updating the basic data file.
The current newly added date data information includes an evaluation condition of a prediction result of the previous day, for example: when the price prediction operation is performed on day 6/month-1, the freight price data of day 6/month-1 may be obtained, and the prediction result of predicting day 6/month-1 on day 5/month-31 may also be obtained, and the prediction result of predicting day 6/month-1 on day 5/month-31 may be used for verification. And obtaining the final new date data information.
S205, performing interpolation fitting on the basic data files, combining historical freight rate data, and constructing data to be predicted.
It should be noted that, the interpolation fitting method for the basic data file may be, but is not limited to, a method of edge deletion + median deletion, which is not limited herein.
S102, updating the holiday list, and removing the unified price adjustment data of the ordinary date in the data to be predicted according to the holiday list to obtain first prediction data.
Wherein the ordinary date is a date not in the holiday list. Holidays are holidays which have an impact on passenger tickets and the dates on which significant events occurred. Valid holiday information affects the fare in the takeoff date dimension.
Optionally, in another embodiment of the present application, an implementation of updating the holiday list, as shown in fig. 3, includes:
s301, reading historical data information when the price prediction operation is executed last time.
And S302, reading a holiday list in the historical data information.
S303, adding the data which is in a row but is not covered by the data in the holiday list in the historical data information into the current holiday list according to the data to be predicted.
Specifically, according to the historical holiday data information in the holiday list in the historical data information, newly-added holiday information is updated to the historical holiday information data, and preparation is made for updating the subsequent holiday list.
Optionally, in another embodiment of the present application, an implementation manner of the method for predicting a price of an international passenger ticket further includes:
and acquiring the total amount of data to be predicted, and reading data information in the current holiday list.
And when the freight rate data jump on the departure date, adjusting the data information in the current holiday list.
The freight rate data is in a jumping state on the departure date, namely, the freight rate data is greatly changed on the departure date.
It should be noted that, adjusting the data information in the current holiday list may be that a significant event occurs when the current date is identified, and the passenger flow may become large, which may result in an air ticket sold out, and therefore, the current date needs to be added to the holiday list; if the current date is identified as an invalid holiday date, the current date and the data information corresponding to the current date need to be deleted in the holiday list.
Optionally, in another embodiment of the present application, an implementation manner of removing the common-day uniform pricing data from the data to be predicted according to the holiday list to obtain the first prediction data is shown in fig. 4, and includes:
s401, determining a search date interval according to the quotation date.
S402, data which does not exist in the search date interval is supplemented nonnumerically.
And S403, forming a two-dimensional matrix in a mode of ascending the departure date and descending the search date.
S404, constructing a two-dimensional array of the search date according to the two-dimensional matrix.
S405, inquiring the freight rate data corresponding to each search date in the two-dimensional array of the search dates.
And S406, judging whether the search dates have unified price adjustment or not according to the freight rate data corresponding to the search dates for each search date.
Specifically, if it is determined that there is a uniform price adjustment on the search date, step S407 is executed.
S407, removing the search date and the freight rate data corresponding to the search date from the two-dimensional array of the search date to obtain first prediction data.
S103, extracting the two-dimensional array in the first prediction data, and predicting the two-dimensional array according to the freight rate time sequence and the historical freight rate time sequence to obtain a prediction result of the common date.
And S104, acquiring freight rate data corresponding to dates in the holiday list from the historical freight rate data according to the holiday list, forming a time sequence, and generating a prediction two-dimensional array.
And S105, carrying out unified price adjustment processing on each date in the predicted two-dimensional array, and determining at least one special freight rate date.
And S106, predicting the special holiday periods aiming at each special freight rate date to obtain a holiday prediction result.
And S107, integrating the prediction result of the common date and the prediction result of the holidays, and outputting the price prediction result of the international passenger ticket on the current day.
And the price prediction result of the international passenger ticket on the current day is the prediction result predicted for N days in the future on the current day. For example: the current day is 5 months and 1 day, N is 2, and then the price prediction result of the international passenger ticket on the current day is the prediction results of 5 months and 2 days and 5 months and 3 days.
The manner of inputting the prediction result of the international ticket of this day may be according to a preset file format, and is diversified and mature, and is not limited herein.
Optionally, in another embodiment of the present application, an implementation manner of the method for predicting a price of an international passenger ticket, as shown in fig. 5, further includes:
s501, receiving a query request of a client.
Wherein the query request includes at least: origin, destination, and departure date.
S502, screening at least one piece of ticket information corresponding to the query request of the client from the price prediction result of the international ticket in the current day, and displaying each piece of ticket information to the client.
According to the scheme, the method for predicting the price of the international passenger ticket comprises the following steps: firstly, determining data to be predicted according to historical freight rate information; then, updating a holiday list, and removing the unified price adjustment data of the ordinary date in the data to be predicted according to the holiday list to obtain first prediction data; wherein the ordinary date is a date not in the holiday list; then, extracting a two-dimensional array in the first prediction data, and predicting the two-dimensional array according to the freight rate time sequence and the historical freight rate time sequence to obtain a prediction result of a common date; acquiring freight rate data corresponding to dates in the holiday list from the historical freight rate data according to the holiday list to form a time sequence and generate a prediction two-dimensional array; carrying out unified price adjustment processing on each date in the predicted two-dimensional array, and determining at least one special freight rate date; predicting the special holiday dates according to each special freight rate date to obtain a prediction result of the holiday dates; and finally, integrating the prediction result of the common date and the prediction result of the holidays, and outputting the price prediction result of the international passenger ticket on the current day. Therefore, the aim of accurately obtaining the price prediction result of the international passenger ticket on the day is fulfilled.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The names of messages or information exchanged between a plurality of devices in the embodiments of the present application are for illustrative purposes only, and are not intended to limit the scope of the messages or information.
Computer program code for carrying out operations for aspects of the present application may be written in any combination of one or more programming languages, including but not limited to an object oriented programming language such as Python, Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
Another embodiment of the present application provides a price predicting device for an international passenger ticket, as shown in fig. 6, specifically including:
the first determining unit 601 is configured to determine data to be predicted according to the historical freight rate information.
Optionally, in another embodiment of the present application, an implementation manner of the first determining unit 601 specifically includes:
a first judgment unit configured to judge whether the currently executed price prediction operation is a first execution price prediction operation.
And the first reading unit is used for reading the full amount of historical freight rate information and constructing a basic data file if the first judging unit judges that the currently executed price predicting operation is the first-time executed price predicting operation.
And the construction unit is used for carrying out interpolation fitting on the basic data file and constructing the data to be predicted.
And the first adding unit is used for adding the current newly added date data information, dividing the historical and future departure date freight rate information and updating the basic data file if the first judging unit judges that the currently executed price prediction operation is not the first-time executed price prediction operation.
And the construction unit is also used for carrying out interpolation fitting on the basic data files, combining the historical freight rate data and constructing the data to be predicted.
For a specific working process of the unit disclosed in the above embodiment of the present application, reference may be made to the content of the corresponding method embodiment, as shown in fig. 2, which is not described herein again.
An updating unit 602, configured to update the holiday list.
Optionally, in another embodiment of the present application, an implementation manner of the updating unit 602 includes:
and a reading unit for reading the history data information when the price prediction operation was performed last time.
And the second reading unit is used for reading the holiday list in the historical data information.
And the second adding unit is used for adding data which is in a row but is not covered by the data in the holiday list in the historical data information into the current holiday list according to the data to be predicted.
For a specific working process of the unit disclosed in the above embodiment of the present application, reference may be made to the content of the corresponding method embodiment, as shown in fig. 3, which is not described herein again.
The first prediction unit 603 is configured to remove the uniform pricing data of the common date from the data to be predicted according to the holiday list to obtain first prediction data.
Wherein the ordinary date is a date not in the holiday list.
Optionally, in another embodiment of the present application, an implementation manner of the first prediction unit 603 includes:
and a third determining unit for determining the search date interval according to the quotation date.
And a supplement unit for performing non-numeric supplement on data which does not exist in the search date section.
And the composition unit is used for composing the two-dimensional matrix in a mode of ascending the departure date and descending the search date.
And the construction unit is used for constructing a two-dimensional array of the search date according to the two-dimensional matrix.
And the query unit is used for querying the freight rate data corresponding to each search date in the two-dimensional array of the search dates.
And the second judging unit is used for judging whether the search dates have unified price adjustment or not according to the freight rate data corresponding to the search dates for each search date.
And the removing unit is used for removing the search date and the freight rate data corresponding to the search date from the two-dimensional array of the search date to obtain the first prediction data if the second judging unit judges that the search date has the unified price adjustment.
For a specific working process of the unit disclosed in the above embodiment of the present application, reference may be made to the content of the corresponding method embodiment, as shown in fig. 4, which is not described herein again.
The extracting unit 604 is configured to extract the two-dimensional array in the first prediction data, and predict the two-dimensional array according to the freight rate time sequence and the historical freight rate time sequence to obtain a prediction result of a common date.
The generating unit 605 is configured to obtain, according to the holiday list, the freight rate data corresponding to the dates in the holiday list from the historical freight rate data, form a time series, and generate a prediction two-dimensional array.
A second determining unit 606, configured to perform de-uniform pricing processing on each date in the predicted two-dimensional array, and determine at least one special freight date.
The second prediction unit 607 is configured to predict the special holiday dates for each special freight date to obtain a prediction result of the holiday dates.
The integration unit 608 is configured to integrate the prediction result of the common date and the prediction result of the holiday, and output the price prediction result of the international ticket on the current day.
For a specific working process of the unit disclosed in the above embodiment of the present application, reference may be made to the content of the corresponding method embodiment, as shown in fig. 1, which is not described herein again.
Optionally, in another embodiment of the present application, an implementation manner of the apparatus for predicting a price of an international ticket further includes:
an acquisition unit for acquiring the total amount of data to be predicted and reading the data information in the current holiday list,
And the adjusting unit is used for adjusting the data information in the current holiday list when the jumping of the freight rate data occurs on the departure date.
For specific working processes of the units disclosed in the above embodiments of the present application, reference may be made to the contents of the corresponding method embodiments, which are not described herein again.
Optionally, in another embodiment of the present application, an implementation manner of the apparatus for predicting a price of an international ticket further includes:
and the receiving unit is used for receiving the query request of the client.
Wherein the query request includes at least: origin, destination, and departure date.
And the display unit is used for screening at least one piece of ticket information corresponding to the query request of the client in the price prediction result of the international ticket at the current day and displaying each piece of ticket information to the client.
For a specific working process of the unit disclosed in the above embodiment of the present application, reference may be made to the content of the corresponding method embodiment, as shown in fig. 5, which is not described herein again.
According to the scheme, the price prediction device of the international passenger ticket comprises the following components: first, the first determination unit 601 determines data to be predicted according to historical freight rate information; then, the updating unit 602 updates the holiday list, and the first prediction unit 603 removes the uniform price adjustment data of the ordinary date in the data to be predicted according to the holiday list to obtain first prediction data; wherein the ordinary date is a date not in the holiday list; then, the extracting unit 604 extracts the two-dimensional array in the first prediction data, and predicts the two-dimensional array according to the freight rate time sequence and the historical freight rate time sequence to obtain a prediction result of a common date; the generating unit 605 acquires freight rate data corresponding to dates in the holiday list from the historical freight rate data according to the holiday list to form a time sequence and generate a prediction two-dimensional array; the second determining unit 606 performs unified price adjustment processing on each date in the predicted two-dimensional array, and determines at least one special freight rate date; the second prediction unit 607 predicts the special holiday dates for each special freight rate date to obtain the prediction results of the holidays; finally, the integration unit 608 integrates the prediction result of the ordinary date and the prediction result of the holiday, and outputs the price prediction result of the international ticket on the current day. Therefore, the aim of accurately obtaining the price prediction result of the international passenger ticket on the day is fulfilled.
The units described in the embodiments of the present application may be implemented by software or hardware. Where the name of a unit does not in some cases constitute a limitation of the unit itself, for example, the first retrieving unit may also be described as a "unit for retrieving at least two internet protocol addresses".
The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), systems on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), and the like.
Another embodiment of the present application provides an electronic device, as shown in fig. 7, including:
one or more processors 701.
A storage 702 having one or more programs stored thereon.
The one or more programs, when executed by the one or more processors 701, cause the one or more processors 701 to implement a method as in any of the above embodiments.
Another embodiment of the present application provides a computer storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method as described in any of the above embodiments.
In the context of this application, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
It should be noted that the computer readable medium mentioned above in the present application may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present application, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In this application, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device.
Another embodiment of the present application provides a computer program product for performing the method of price prediction of international tickets as defined in any one of the preceding claims when the computer program product is executed.
In particular, according to embodiments of the application, the processes described above with reference to the flow diagrams may be implemented as computer software programs. For example, embodiments of the present application include a computer program product comprising a computer program carried on a non-transitory computer readable medium, the computer program containing program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication means, or installed from a storage means, or installed from a ROM. The computer program, when executed by a processing device, performs the above-described functions defined in the method of the embodiments of the present application.
Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.
While several specific implementation details are included in the above discussion, these should not be construed as limitations on the scope of the application. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination.
The above description is only a preferred embodiment of the application and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the application referred to in the present application is not limited to the embodiments with a particular combination of the above-mentioned features, but also encompasses other embodiments with any combination of the above-mentioned features or their equivalents without departing from the scope of the application. For example, the above features may be replaced with (but not limited to) features having similar functions as those described in this application.
Claims (10)
1. A method for predicting the price of an international passenger ticket is characterized by comprising the following steps:
determining data to be predicted according to historical freight rate information;
updating a holiday list, and removing unified price adjustment data of an ordinary date in the data to be predicted according to the holiday list to obtain first prediction data; wherein the ordinary date is a date not in the holiday list;
extracting a two-dimensional array in the first prediction data, and predicting the two-dimensional array according to the freight rate time sequence and the historical freight rate time sequence to obtain a prediction result of a common date;
acquiring freight rate data corresponding to dates in the holiday list from historical freight rate data according to the holiday list, forming a time sequence, and generating a prediction two-dimensional array;
carrying out unified price adjustment processing on each date in the predicted two-dimensional array, and determining at least one special freight rate date;
predicting the special holiday dates according to each special freight rate date to obtain a prediction result of the holidays;
and integrating the prediction result of the common date and the prediction result of the holiday, and outputting the price prediction result of the international passenger ticket on the current day.
2. The prediction method according to claim 1, wherein the determining the data to be predicted according to the historical freight rate information comprises:
judging whether the currently executed price prediction operation is the first execution price prediction operation;
if the currently executed price prediction operation is judged to be the first-time execution price prediction operation, reading the full-amount historical freight rate information and constructing a basic data file;
performing interpolation fitting on the basic data file to construct data to be predicted;
if the currently executed price prediction operation is judged not to be the first time price prediction operation, adding the currently newly added date data information, dividing the historical and future departure date freight rate information, and updating the basic data file;
and carrying out interpolation fitting on the basic data files, and combining historical freight rate data to construct data to be predicted.
3. The prediction method of claim 1, wherein the updating the holiday list comprises:
reading in historical data information when price prediction operation is executed last time;
reading a holiday list in the historical data information;
adding data which is in line but not covered by data in the holiday list in the historical data information to a current holiday list according to the data to be predicted.
4. The prediction method according to claim 3, further comprising:
acquiring the total amount of data to be predicted, and reading data information in a current holiday list;
and when the freight rate data jump on the departure date, adjusting the data information in the current holiday list.
5. The prediction method according to claim 1, wherein the removing, according to the holiday list, the common-day uniform pricing data in the data to be predicted to obtain first prediction data comprises:
determining the interval of the search date according to the quotation date;
performing non-numeric supplementation on data which does not exist in the search date interval;
forming a two-dimensional matrix in a mode of ascending the departure date and descending the search date;
constructing a two-dimensional array of the search date according to the two-dimensional matrix;
inquiring the freight rate data corresponding to each search date in the two-dimensional array of the search dates;
aiming at each search date, judging whether unified price adjustment exists on the search date according to the freight rate data corresponding to the search date;
and if the search date is judged to have the unified price adjustment, removing the search date and the freight rate data corresponding to the search date from the two-dimensional array of the search date to obtain first prediction data.
6. The forecasting method according to claim 1, wherein the integrating the forecast result of the common date and the forecast result of the holiday and outputting the forecast result of the price of the international ticket on the current day further comprises:
receiving a query request of a client; wherein the query request includes at least: a departure location, a destination, and a departure date;
and screening at least one piece of ticket information corresponding to the query request of the client in the price prediction result of the international ticket in the current day, and displaying each piece of ticket information to the client.
7. An apparatus for predicting the price of an international ticket, comprising:
the first determining unit is used for determining data to be predicted according to the historical freight rate information;
the updating unit is used for updating the holiday list;
the first prediction unit is used for removing the unified price adjusting data of the ordinary date in the data to be predicted according to the holiday list to obtain first prediction data; wherein the ordinary date is a date not in the holiday list;
the extraction unit is used for extracting the two-dimensional array in the first prediction data, predicting the two-dimensional array according to the freight rate time sequence and the historical freight rate time sequence, and obtaining a prediction result of a common date;
the generating unit is used for acquiring freight rate data corresponding to dates in the holiday list from historical freight rate data according to the holiday list, forming a time sequence and generating a prediction two-dimensional array;
the second determining unit is used for carrying out unified price adjustment processing on each date in the prediction two-dimensional array and determining at least one special freight rate date;
the second prediction unit is used for predicting the special holiday dates according to each special freight rate date to obtain a prediction result of the holidays;
and the integration unit is used for integrating the prediction result of the common date and the prediction result of the holiday and outputting the price prediction result of the international passenger ticket on the current day.
8. The prediction apparatus according to claim 7, wherein the first determination unit includes:
a first judgment unit configured to judge whether or not a currently executed price prediction operation is a first execution price prediction operation;
the first reading unit is used for reading the full-amount historical freight rate information and constructing a basic data file if the first judging unit judges that the currently executed price prediction operation is the first-time executed price prediction operation;
the construction unit is used for carrying out interpolation fitting on the basic data file and constructing data to be predicted;
a first adding unit, configured to add current newly added date data information, divide history and future departure date freight rate information, and update the basic data file, if the first determining unit determines that the currently executed price prediction operation is not the first-time executed price prediction operation;
the construction unit is also used for carrying out interpolation fitting on the basic data files, combining historical freight rate data and constructing data to be predicted.
9. An electronic device, comprising:
one or more processors;
a storage device having one or more programs stored thereon;
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method for price forecasting of international tickets according to any of claims 1 to 6.
10. A computer storage medium, having stored thereon a computer program, wherein the computer program, when being executed by a processor, carries out a method of price prediction of international tickets according to any one of claims 1 to 6.
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