A Bayesian Approach for Flight Fare Prediction Based on Kalman Filter

  • Abhijit BoruahEmail author
  • Kamal Baruah
  • Biman Das
  • Manash Jyoti Das
  • Niranjan Borpatra Gohain
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 714)


Decision-making under uncertainty is one of the major issues faced by recent computer-aided solutions and applications. Bayesian prediction techniques come handy in such areas of research. In this paper, we have tried to predict flight fares using Kalman filter which is a famous Bayesian estimation technique. This approach presents an algorithm based on the linear model of the Kalman Filter. This model predicts the fare of a flight based on the input provided from an observation of previous fares. The observed data is given as input in the form of a matrix as required to the linear model, and an estimated fare for a specific upcoming flight is calculated.


Flight fare Observation Prediction Kalman filter Linear model 


  1. 1.
    Feng, Youyi, and Baichun Xiao. “A dynamic airline seat inventory control model and its optimal policy.” Operations Research 49.6 (2001): 938–949.Google Scholar
  2. 2.
    Etzioni, Oren, et al. “To buy or not to buy: mining airfare data to minimize ticket purchase price.” Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2003.Google Scholar
  3. 3.
    Rama-Murthy, Krishna. Modeling of United States Airline Fares–Using the Official Airline Guide (OAG) and Airline Origin and Destination Survey (DB1B). Diss. Virginia Tech, 2006.Google Scholar
  4. 4.
    Groves, William, and Maria Gini. “A regression model for predicting optimal purchase timing for airline tickets.” Technical report (2011).Google Scholar
  5. 5.
    Diebold, Francis X. Elements of forecasting. South-Western College Publ., 2006.Google Scholar
  6. 6.
    Maybeck, Peter S. “The Kalman filter: An introduction to concepts.” Autonomous robot vehicles. Springer New York, 1990. 194–204.Google Scholar
  7. 7.
    “QuantStart.” State Space Models and the Kalman Filter - QuantStart. N.p., n.d. Web. 10 May 2017.Google Scholar
  8. 8.
    “An Explanation of the Kalman Filter.” Mathematics Stack Exchange. N.p., n.d. Web. 10 May 2017.Google Scholar
  9. 9.
    Harrison, Jeff, and Mike West. Bayesian forecasting & dynamic models. New York: Springer, 1999.Google Scholar
  10. 10.
    “State Estimation with a Kalman Filter.” Web. 10 May 2017.
  11. 11.
    “Linear Time-Invariant Systems.” Stanford University. N.p., n.d. Web. 10 May 2017.Google Scholar
  12. 12.
    Kosanam, Srikiran, and Daniel J. Simon. “Kalman filtering with uncertain noise covariances.” (2004): 375.)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Abhijit Boruah
    • 1
    Email author
  • Kamal Baruah
    • 1
  • Biman Das
    • 1
  • Manash Jyoti Das
    • 1
  • Niranjan Borpatra Gohain
    • 1
  1. 1.Department of Computer Science and EngineeringDUIET, Dibrugarh UniversityDibrugarhIndia

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