Airport Trends Analytics Engine using the ARIMA Model
DOI:
https://doi.org/10.14419/ijet.v7i3.12.16033Published:
2018-07-20Keywords:
ARIMAmodel, Time series analysis, Airport Trends prediction, Air Cargo Movement, Air Passenger Traffic, Long-termprediction.Abstract
Data Analytics is the process of analyzing unprocessed data to draw conclusions by studying and inspecting various patterns in the data. Several algorithms and conceptual methods are often followed to derive legit and accurate results. Efficient data handling is important for interactive visualization of data sets. Considering recent researches and analytical theories on column-oriented Database Management System, we are developing a new data engine using R and Tableau to predict airport trends. The engine uses Univariate datasets (Example, Perth Airport Passenger Movement Dataset, and Newark Airport Cargo Stats Dataset) to analyze and predict accurate trends. Data analyzing and prediction is done with the implementation of Time Series Analysis and respective ARIMA Models for respective modules. Development of modules is done using RStudio whereas Tableau is used for interactive visualization and end-user report generation. The Airport Trends Analytics Engine is an integral part of R and Tableau 10.4 and is optimized for use on desktop and server environments.
References
[1] Ayodele A. Adebiyi., Aderemi O. Adewumi and Charles K. Ayo, “Stock Price Prediction Using the ARIMA Modelâ€, UKSim-AMSS 16th International Conference on Computer Modelling and Simulation, pp. 105-111, 2014.
[2] Peng Chen, Hongyong Yuan and Xueming Shu, “Forecasting Crime Using the ARIMA Modelâ€, Fifth International Conference on Fuzzy Systems and Knowledge Discovery, pp. 627-630, 2008
[3] Mirjana Ivanović and Vladimir Kurbalija, “Time series analysis and possible applicationsâ€, 39th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 473-479, 2016
[4] Xing-qiang Zhang, Xue Yang and Shi-qing Dong, “Study on composite forecasting model of air passenger capacity based on air partitionâ€, pp. V9-66–V9-69, 2010
[5] M O D Rizwan, R. Jeberson Retna Raj and M Vasudev, “A Novel Approach For Time Series Data Forecasting Based On Arima Model For Marine Fishesâ€, International Conference on Algorithms, Methodology, Models and Applications in Emerging Technologies (ICAMMAET), 2017
[6] Richard Wesley, Matthew Eldridge and Pawel T. Terlecki, “An Analytic Data Engine for Visualization in Tableauâ€, SIGMOD '11 Proceedings of the 2011 ACM SIGMOD International Conference on Management of data, pp. 1185-1194, 2011
How to Cite
License
Authors who publish with this journal agree to the following terms:- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution Licensethat allows others to share the work with an acknowledgement of the work''s authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal''s published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
Accepted 2018-07-22
Published 2018-07-20