A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf
.
Forecasting monthly airline passenger numbers with small datasets using feature engineering and a modified principal component analysis
2020
In this study, a machine learning approach based on time series models, different feature engineering, feature extraction, and feature derivation is proposed to improve air passenger forecasting. Different types of datasets were created to extract new features from the core data. An experiment was undertaken with artificial neural networks to test the performance of neurons in the hidden layer, to optimise the dimensions of all layers and to obtain an optimal choice of connection weights – thus
doi:10.26174/thesis.lboro.12249779.v1
fatcat:2okw5ezn5vayfldw3h5bbzxly4