CN105844346A - Flight delay prediction method based on ARIMA model - Google Patents
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- 230000003111 delayed effect Effects 0.000 claims description 46
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Abstract
The invention relates to a flight delay prediction method based on an ARIMA model. The method comprises the following steps of stepS1, establishing a data set: flight data is collected and the flight data includes flight information, time information, airport information and delay time information; stepS2, analyzing a data set characteristic: differences of airports and airlines and an influence of weather on the flight are analyzed; stepS3, establishing the ARIMA model: establishing of the ARIMA model includes a mobile average process, an autoregressive process, an autoregressive mobile average process and an ARIMA process; stepS4, selecting an optimum ARIMA model: the established ARIMA model is verified and an optimum parameter is selected; stepS5, predicting flight delay: any flight data is acquired and then an optimum ARIMA function is selected to be a model delay function, a multiple-linear regression function is selected to be a weather delay function and a final prediction result is acquired after addition of the functions. By using the method, high prediction precision can be realized, usage time is shortened and flight delay can be effectively predicted.
Description
Technical field
The present invention relates to Flight Information analysis technical field, be specifically related to a kind of flight based on ARIMA model and be delayed pre-
Survey method.
Background technology
Effective data mining algorithm and analysis strategy, can be company by obtaining priori or individual extracts and has valency
The information of value, and help them to make further decision.In those relate to the field of big data, flight has been delayed prediction
Cause and pay close attention to widely.In recent years, the importance of delayed risk management is apparent.Such as, appalling
The generation of MH370 aviation accident, and domestic flight is delayed the tremendous economic that causes and loses and bring to airline and passenger
Huge discontented, shift flight risk management onto a position the most urgent.So no matter from safety coefficient or economy side
For face, it is the most necessary that more effective flight is delayed forecast model.
But, in view of the feature (such as big, the multiformity of data volume etc.) of flight data, prediction flight is delayed accurately, with
Time ensure computational complexity and postpone be highly difficult within the acceptable range.Additionally, affect in delayed feature, sky
The factors such as gas may dynamically change.It is therefore proposed that flight based on ARIMA model is delayed Forecasting Methodology, it can be effectively
Prediction flight is delayed.
Summary of the invention
In view of this, it is an object of the invention to provide a kind of flight based on ARIMA model and be delayed Forecasting Methodology, to realize
Higher precision of prediction, shortens the use time, and effectively prediction flight is delayed.
The present invention uses below scheme to realize: a kind of flight based on ARIMA model be delayed Forecasting Methodology, specifically include with
Lower step:
Step S1: set up data set: gather flight data, described flight number include Flight Information, temporal information, Airport information with
And delay time at stop information；
Step S2: data set features analysis: analyze the impact on flight of the difference between each airport and airline and weather；
Step S3: set up ARIMA model: the foundation of ARIMA model includes moving average process MA, autoregressive process AR, certainly returns
Return moving average process ARMA and ARIMA process；
Step S4: choose optimal ARIMA model: the ARIMA model set up is verified and chooses optimal parameter；
Step S5: flight is delayed prediction: after obtaining any one flight data, chooses optimal ARIMA function and is delayed as model
Function, chooses Multiple Linear Regression Function as weather delay function, and by described model delay function and weather delay function
It is added, obtains last predicting the outcome.
Further, in described step S1, described Flight Information includes airline, flight number and airplane tail number；Institute
State temporal information to include date, E. T. D., E.T.A, estimated flight time, actual flying time, be delayed
Send out the time and be delayed the time of advent；Described Airport information include departure airfield, arrive at the airport, the tax rate time limit, the departure time with
And the landing time；Delay time at stop information includes extreme weather conditions, the aircraft of airline controls situation, national airspace system
Aircraft controls situation and safety problem.
Further, described extreme weather conditions includes tornado and snowstorm；The aircraft of described airline controls feelings
Condition includes aircraft maintenance or cleaning and loading baggage；The aircraft of described national airspace system controls situation and includes Aerodrome Operations and friendship
Course line is cancelled under pass blocking plug；Described safety problem includes the security breaches of aircraft.
Further, in described step S3, described ARIMA model is according to former sequence contained portion whether steadily and in returning
The difference divided, including moving average process MA, autoregressive process AR, autoregressive moving-average (ARMA) process ARMA and ARIMA process；
Acyclic ARIMA model is divided into ARIMA, and (p, d, q) model, wherein p is autoregression item, and q is rolling average item
Number, the difference number of times done when d becomes steady by time series, the most described ARIMA model is as follows:
Wherein；For autoregression (AR)
Coefficient polynomial；For rolling average (MA) coefficient polynomial；For zero-mean white noise sequence.
Further, described step S4 specifically includes following steps:
Step S41: carry out data stationarity inspection: draw time series and figure be evaluated or use ADF unit root to enter
Performing check；
Step S42: matching stationary time series ARMA: { y} represents the stationary time series carried out after difference, and calculates in use
Auto-correlation function ACF and partial autocorrelation function PACF, selects the value of suitable p and q from ACF and PACF test pattern；
Step S43: Selection parameter: the various combination of test p and q, application AIC and SC criterion selects optimal model parameter；
Step S44: modelling verification: by checking whether residual error is that a white noise sequence verifies that this model is the most accurate；
Step S45: model prediction: according to chosen model, make a prediction for the future value that flight model is delayed.
Further, described step S5 specifically includes following steps:
Step S51: Selection Model delay function: select ARIMA function as model delay function；
Step S52: choose weather delay: obtain delayed three of impact by principal component analysis and factor approach
Principal element, described three principal elements include wind, visibility and air conditions, use Multiple Linear Regression Function as weather
Delay function:
WhereinRefer to wind speed,Refer to visibility,Refer to air conditions andRepresent flight number.
Step S53: apply all weather histories be delayed data set linear regression and minimize probable value, it is thus achieved that reasonably,
Secondly setting up the regression equation being applicable to weather delay, then the advantage of analytic function, the inspection correctness of regression function, sky are depressed
The randomness of fruit and the special correctness returning co-factor, finally make a prediction to the future value of flight weather delay.
Compared with prior art, the present invention based on ARIMA(difference auto regressive moving average) flight of model is delayed pre-
Survey method, it is achieved higher precision of prediction, shortens the use time, and effectively prediction flight is delayed.
Accompanying drawing explanation
Fig. 1 is the schematic flow sheet of the present invention.
Detailed description of the invention
Below in conjunction with the accompanying drawings and embodiment the present invention will be further described.
This enforcement provides a kind of flight based on ARIMA model to be delayed Forecasting Methodology, as it is shown in figure 1, specifically include following
Step:
Step S1: set up data set: gather flight data, described flight number include Flight Information, temporal information, Airport information with
And delay time at stop information；
Step S2: data set features analysis: analyze the impact on flight of the difference between each airport and airline and weather；
Step S3: set up ARIMA model: the foundation of ARIMA model includes moving average process MA, autoregressive process AR, certainly returns
Return moving average process ARMA and ARIMA process；
Step S4: choose optimal ARIMA model: the ARIMA model set up is verified and chooses optimal parameter；
Step S5: flight is delayed prediction: after obtaining any one flight data, chooses optimal ARIMA function and is delayed as model
Function, chooses Multiple Linear Regression Function as weather delay function, and by described model delay function and weather delay function
It is added, obtains last predicting the outcome.
In the present embodiment, in described step S1, described Flight Information includes airline, flight number and airplane tail
Number；Described temporal information include the date, the E. T. D., E.T.A, estimated flight time, actual flying time,
It is delayed the departure time and is delayed the time of advent；Described Airport information includes departure airfield, arrives at the airport, the tax rate time limit, takes off
Time and landing time；Delay time at stop information includes extreme weather conditions, the aircraft of airline controls situation, state aviation
The aircraft of system controls situation and safety problem.
In the present embodiment, described extreme weather conditions includes tornado and snowstorm；The aircraft control of described airline
Situation processed includes aircraft maintenance or cleaning and loading baggage；The aircraft of described national airspace system controls situation and includes Aerodrome Operations
With cancellation course line under traffic jam；Described safety problem includes the security breaches of aircraft.
In the present embodiment, described step S2, by the analysis to some airports Yu airline, can be concluded that
1, airport pattern: the flight quantity on each airport, mean delay time, airline's quantity and cancellation rate there are differences.2, aviation
Pattern: the airline of the U.S. can than Southwest Airlines Co have one higher set out/arrive delay rate and longer delay time
Between.3, weather impact: according to statistics, the flight of 4 percent is delayed the impact being because extreme weather, rather than extreme weather also can
Causing aircraft late, weather is for affecting a delayed key factor.
In the present embodiment, arma modeling includes autoregression (AR) and rolling average (MA).The difference of ARMA Yu ARIMA
For: arma modeling is used for predicting stationary time series, and ARIMA is used for predicting nonstationary time series.ARIMA model is difference
With the combination of ARMA, referred to as difference ARMA model, refer to be converted into nonstationary time series stationary time sequence
Row, then only return set up mould to its lagged value and the present worth of stochastic error and lagged value by dependent variable
Type.
In the most described step S3, whether contained part is not the most steadily and in returning according to former sequence for described ARIMA model
With, including moving average process MA, autoregressive process AR, autoregressive moving-average (ARMA) process ARMA and ARIMA process；By non-week
The ARIMA model of phase property is divided into ARIMA (p, d, q) model
, wherein p is autoregression item, and q is rolling average item number, the difference number of times done when d becomes steady by time series, then institute
State ARIMA model as follows:
Wherein；For autoregression (AR) it is
Number multinomial；For rolling average (MA) coefficient polynomial；For zero-mean white noise sequence.
In the present embodiment, described step S4 specifically includes following steps:
Step S41: carry out data stationarity inspection: a simple method draws time series exactly, and comments figure
Valency, the most accurate method uses ADF unit root to test exactly；
Step S42: matching stationary time series ARMA: { y} represents the stationary time series carried out after difference, and calculates in use
Auto-correlation function ACF and partial autocorrelation function PACF, selects the value of suitable p and q from ACF and PACF test pattern；
Step S43: Selection parameter: the various combination of test p and q, application AIC and SC criterion selects optimal model parameter；
Step S44: modelling verification: by checking whether residual error is that a white noise sequence verifies that this model is the most accurate；
Step S45: model prediction: according to chosen model, make a prediction for the future value that flight model is delayed.
In the present embodiment, in described step S5, after obtaining flight data, extract two and mainly result in delayed spy
Levy vector, be predicted, specifically include following steps:
Step S51: Selection Model delay function: select ARIMA function as model delay function；Wherein said ARIMA function
Choose i.e. according to said method obtain: first draw flight model be delayed data profile, carry out data stationarity inspection,
Determine whether stationary time series；As if it is not, then carry out time series difference, until obtaining a stable time sequence
Row；Secondly select suitable ARIMA model, use " acf " and " pacf " function in R to distinguish (certainly) relevant figure and partial correlation
Figure, and the actual value of auto-correlation and partial correlation is obtained at " acf " and " pacf " setting " plot=FALSE ".Moreover use in R
" arima () " function estimate ARIMA (p, d, q) parameter in model, select optimum model parameter, use residual error checking mould
Type correctness, finally uses them to make prediction model, and the future value being delayed flight model is made a prediction；
Step S52: choose weather delay: obtain delayed three of impact by principal component analysis and factor approach
Principal element, described three principal elements include wind, visibility and air conditions, use Multiple Linear Regression Function as weather
Delay function:
WhereinRefer to wind speed,Refer to visibility,Refer to air conditions andRepresent flight number.
Step S53: apply all weather histories be delayed data set linear regression and minimize probable value, it is thus achieved that reasonably,
Secondly setting up the regression equation being applicable to weather delay, then the advantage of analytic function, the inspection correctness of regression function, sky are depressed
The randomness of fruit and the special correctness returning co-factor, finally make a prediction to the future value of flight weather delay.
The foregoing is only presently preferred embodiments of the present invention, all impartial changes done according to scope of the present invention patent with
Modify, all should belong to the covering scope of the present invention.
Claims (6)
1. a flight based on ARIMA model is delayed Forecasting Methodology, it is characterised in that: specifically include following steps:
Step S1: set up data set: gather flight data, described flight number include Flight Information, temporal information, Airport information with
And delay time at stop information；
Step S2: data set features analysis: analyze the impact on flight of the difference between each airport and airline and weather；
Step S3: set up ARIMA model: the foundation of ARIMA model includes moving average process MA, autoregressive process AR, certainly returns
Return moving average process ARMA and ARIMA process；
Step S4: choose optimal ARIMA model: the ARIMA model set up is verified and chooses optimal parameter；
Step S5: flight is delayed prediction: after obtaining any one flight data, chooses optimal ARIMA function and is delayed as model
Function, chooses Multiple Linear Regression Function as weather delay function, and by described model delay function and weather delay function
It is added, obtains last predicting the outcome.
A kind of flight based on ARIMA model the most according to claim 1 is delayed Forecasting Methodology, it is characterised in that: described
In step S1, described Flight Information includes airline, flight number and airplane tail number；Described temporal information includes date, pre-
When meter departure time, E.T.A, estimated flight time, actual flying time, delay departure time and delay arrive
Between；Described Airport information include departure airfield, arrive at the airport, the tax rate time limit, the departure time and landing time；Delay time at stop
Information includes extreme weather conditions, the aircraft of airline controls situation, the aircraft of national airspace system controls situation and peace
Full problem.
A kind of flight based on ARIMA model the most according to claim 2 is delayed Forecasting Methodology, it is characterised in that: described
Extreme weather conditions includes tornado and snowstorm；The aircraft of described airline control situation include aircraft maintenance or cleaning with
Loading baggage；The aircraft of described national airspace system controls situation and includes cancelling course line under Aerodrome Operations and traffic jam；Described
Safety problem includes the security breaches of aircraft.
A kind of flight based on ARIMA model the most according to claim 1 is delayed Forecasting Methodology, it is characterised in that: described
In step S3, described ARIMA model is according to former sequence difference of contained part whether steadily and in returning, including rolling average
Process MA, autoregressive process AR, autoregressive moving-average (ARMA) process ARMA and ARIMA process；By acyclic ARIMA model
(p, d, q) model, wherein p is autoregression item, and q is rolling average item number, and d is that time series becomes steady to be divided into ARIMA
Time the difference number of times that done, the most described ARIMA model is as follows:
Wherein；For from returning
Return (AR) coefficient polynomial；For rolling average (MA) it is
Number multinomial；For zero-mean white noise sequence.
A kind of flight based on ARIMA model the most according to claim 1 is delayed Forecasting Methodology, it is characterised in that: described
Step S4 specifically includes following steps:
Step S41: carry out data stationarity inspection: draw time series and figure be evaluated or use ADF unit root to enter
Performing check；
Step S42: matching stationary time series ARMA: { y} represents the stationary time series carried out after difference, and calculates in use
Auto-correlation function ACF and partial autocorrelation function PACF, selects the value of suitable p and q from ACF and PACF test pattern；
Step S43: Selection parameter: the various combination of test p and q, application AIC and SC criterion selects optimal model parameter；
Step S44: modelling verification: by checking whether residual error is that a white noise sequence verifies that this model is the most accurate；
Step S45: model prediction: according to chosen model, make a prediction for the future value that flight model is delayed.
A kind of flight based on ARIMA model the most according to claim 1 is delayed Forecasting Methodology, it is characterised in that: described
Step S5 specifically includes following steps:
Step S51: Selection Model delay function: select ARIMA function as model delay function；
Step S52: choose weather delay: obtain delayed three of impact by principal component analysis and factor approach
Principal element, described three principal elements include wind, visibility and air conditions, use Multiple Linear Regression Function as weather
Delay function:
WhereinRefer to wind speed,Refer to visibility,Refer to air conditions andRepresent flight number；
Step S53: apply all weather histories be delayed data set linear regression and minimize probable value, it is thus achieved that reasonably, secondly
Set up and be applicable to the regression equation of weather delay, then the advantage of analytic function, the inspection correctness of regression function, weather result
Randomness and the special correctness returning co-factor, finally make a prediction to the future value of flight weather delay.
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| CN108615411A (en) * | 2016-12-13 | 2018-10-02 | 北京航管科技有限公司 | a kind of method and device for realizing flight information processing |
| CN106408139A (en) * | 2016-12-20 | 2017-02-15 | 中国人民解放军空军装备研究院雷达与电子对抗研究所 | Airport arrival rate prediction method and device |
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| CN107547269A (en) * | 2017-08-14 | 2018-01-05 | 浙江大学 | The construction method of intelligent substation communication flow threshold model based on FARIMA |
| CN107547269B (en) * | 2017-08-14 | 2020-06-30 | 浙江大学 | Method for constructing intelligent substation communication flow threshold model based on FARIMA |
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