Airport Trends Analytics Engine using the ARIMA Model
DOI:
https://doi.org/10.14419/ijet.v7i3.12.16033Keywords:
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.
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Received date: July 22, 2018
Accepted date: July 22, 2018
Published date: July 20, 2018