Forecasting monthly airline passenger numbers with small datasets using feature engineering and a modified principal component analysis

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 the nonlinear optimisation problem could be solved directly. A method of tuning deep learning models using H2O (which is a feature-rich, open source machine learning platform known for its R and Spark integration and its ease of use) is also proposed, where the trained network model is built from samples of selected features from the dataset in order to ensure diversity of the samples and to improve training. A successful application of deep learning requires setting numerous parameters in order to achieve greater model accuracy. The number of hidden layers and the number of neurons, are key parameters in each layer of such a network. Hyper-parameter, grid search, and random hyper-parameter approaches aid in setting these important parameters. Moreover, a new ensemble strategy is suggested that shows potential to optimise parameter settings and hence save more computational resources throughout the tuning process of the models. The main objective, besides improving the performance metric, is to obtain a distribution on some hold-out datasets that resemble the original distribution of the training data. Particular attention is focused on creating a modified version of Principal Component Analysis (PCA) using a different correlation matrix – obtained by a different correlation coefficient based on kinetic energy to derive new features. The data were collected from several airline datasets to build a deep prediction model for forecasting airline passenger numbers. Preliminary experiments show that fine-tuning provides an efficient approach for tuning the ultimate number of hidden layers and the number of neurons in each layer when compared with the grid search method. Similarly, the results show that the modified version of PCA is more effective in data dimension reduction, classes reparability, and classification accuracy than using traditional PCA.</div

Time Series Event Forecasting in Consumer Electronic Markets using Random Forests

Consumers are price-sensitive and opportunistic about the place of purchase when buying electronic goods. However, services that advise customers on their purchase time decisions for those products are missing. Given the objective to provide a binary signal to customers to either wait or purchase immediately, classification algorithms are a direct methodological choice. Approaches like random forests allow for the derivation of a probability and class prediction but are usually not used in time series contexts. This is due to missing or time-invariant regressors and unclear prediction settings. We show how classification methods can be used to generate reliable predictions of price events and analyze if they are subject to common market dependencies. Pooling univariate random forests and enhancing them with multivariate features shows that our approach generates stable and valuable recommendations. Because dependency structures between products are transferable, multivariate forecasting increases accuracy and issues recommendations where univariate approaches fail

A forecasting Tool for Predicting Australia\u27s Domestic Airline Passenger Demand Using a Genetic Algorithm

This study has proposed and empirically tested for the first time genetic algorithm optimization models for modelling Australia’s domestic airline passenger demand, as measured by enplaned passengers (GAPAXDE model) and revenue passenger kilometres performed (GARPKSDE model). Data was divided into training and testing datasets; 74 training datasets were used to estimate the weighting factors of the genetic algorithm models and 13 out-of-sample datasets were used for testing the robustness of the genetic algorithm models. The genetic algorithm parameters used in this study comprised population size (n): 200; the generation number: 1,000; and mutation rate: 0.01. The modelling results have shown that both the quadratic GAPAXDE and GARPKSDE models are more accurate, reliable, and have greater predictive capability as compared to the linear models. The mean absolute percentage error in the out of sample testing dataset for the GAPAXDE and GARPKSDE quadratic models are 2.55 and 2.23%, respectively

A machine learning approach to itinerary-level booking prediction in competitive airline markets

Demand forecasting is extremely important in revenue management. After all, it is one of the inputs to an optimisation method which aim is to maximize revenue. Most, if not all, forecasting methods use historical data to forecast the future, disregarding the "why". In this paper, we combine data from multiple sources, including competitor data, pricing, social media, safety and airline reviews. Next, we study five competitor pricing movements that, we hypothesize, affect customer behavior when presented a set of itineraries. Using real airline data for ten different OD-pairs and by means of Extreme Gradient Boosting, we show that customer behavior can be categorized into price-sensitive, schedule-sensitive and comfort ODs. Through a simulation study, we show that this model produces forecasts that result in higher revenue than traditional, time series forecasts

The Collegian (2003-01-20)

Incomplete in digital format.https://scholarworks.utrgv.edu/collegian/1231/thumbnail.jp

Developing advanced methods to predict air traffic network growth

This dissertation describes a forecasting methodology that takes into account changes in the connectivity of an air transportation system and assesses the impact at other levels of the network, such as route demand and air traffic levels. To achieve this, the modelling framework looks at city pair demand generation, route demand assignment and air traffic estimation. While generating air traffic forecasts, the resulting model is also intended to highlight the most important factors driving air traffic network growth. This is achieved by considering a larger set of drivers than those considered in existing methodologies and research as well as exploring the use of alternative modelling techniques. Network evolution is incorporated in the method through an airport connectivity model which identifies how and when airport-pairs across the network change their connectivity status. The problem is split into two models: one identifying those airport-pairs that are added to the network; and another one identifying those airport-pairs that are removed from the network. The modelling approach explores the use of network theory metrics along with other input variables, such as passenger demand, to see whether existing models employing only network theory metrics could be improved. The impact of network evolution is assessed by the effect on air itinerary shares. Two itinerary choice models are developed using two different modelling approaches: multinomial logit and neural networks. While the multinomial logit formulation is the most common approach used to model itinerary shares, only few studies have looked at modelling itinerary shares at the network level. Neural networks have yet to be explored in this field. In this research, air itinerary choice models have been developed at the most aggregate level, using open-source booking data, for a large group of city-pairs within the US Air Transportation System. The output of the itinerary choice models, influenced by the consideration of network evolution, is then used to project air traffic levels and assess the impact of network structure changes in the number of operations in the US ATS. The results reflect the complexity behind network evolution, especially for cases when a mature system is considered (e.g. US ATS): comparisons between the case of a static network and the case when network evolution is considered indicate that the impact of network changes on overall system metrics is relatively minor in the US. However, they indicate that changes in fossil fuel prices may influence changes in the overall network characteristics, and consequently network evolution. The results also prove the feasibility of estimating a single itinerary choice model at the network level for an entire air transportation system. Although the multinomial logit model results have better accuracy, the potential of neural networks for this purpose is also demonstrated, the latter being more representative of the hub-and-spoke network strategy

Spartan Daily, March 21, 1991

Volume 96, Issue 37https://scholarworks.sjsu.edu/spartandaily/8105/thumbnail.jp

Utilizing advanced modelling approaches for forecasting air travel demand: a case study of Australia’s domestic low cost carriers

One of the most pervasive trends in the global airline industry over the past few three decades has been the rapid development of low cost carriers (LCCs). Australia has not been immune to this trend. Following deregulation of Australia&amp;rsquo;s domestic air travel market in the 1990s, a number of LCCs have entered the market, and these carriers have now captured around 31 per cent of the market. Australia&amp;rsquo;s LCCs require reliable and accurate passenger demand forecasts as part of their fleet, network, and commercial planning and for scaling investments in fleet and their associated infrastructure. Historically, the multiple linear regression (MLR) approach has been the most popular and recommended method for forecasting airline passenger demand. In more recent times, however, new advanced artificial intelligence-based forecasting approaches &amp;ndash; artificial neural networks (ANNs), genetic algorithm (GA), and adaptive neuro-fuzzy inference system (ANFIS) - have been applied in a broad range of disciplines. In light of the critical importance of passenger demand forecasts for airline management, as well as the recent developments in artificial intelligence-based forecasting methods, the key aim of this thesis was to specify and empirically examine three artificial intelligence-based approaches (ANNs, GA and ANFIS) as well as the MLR approach, in order to identify the optimum model for forecasting Australia&amp;rsquo;s domestic LCCs demand. This is the first time that such models &amp;ndash; enplaned passengers (PAX) and revenue passenger kilometres performed (RPKs) &amp;ndash; have been proposed and tested for forecasting Australia&amp;rsquo;s domestic LCCs demand. The results show that of the four modeling approaches used in this study that the new, and novel, ANFIS approach provides the most accurate, reliable, and highest predictive capability for forecasting Australia&amp;rsquo;s LCCs demand. A second aim of the thesis was to explore the principal determinants of Australia&amp;rsquo;s domestic LCCs demand in order to achieve a greater understanding of the factors which influence air travel demand. The results show that the primary determinants of Australia&amp;rsquo;s domestic LCCs demand are real best discount airfare, population, real GDP, real GDP per capita, unemployment, world jet fuel prices, real interest rates, and tourism attractiveness. Interestingly three determinants, unemployment, tourism attractiveness, and real interest rates, which have not been empirically examined in any previously reported study of Australia&amp;rsquo;s domestic LCCs demand, proved to be important predictor variables of Australia&amp;rsquo;s domestic LCCs demand. The thesis also found that Australia&amp;rsquo;s LCCs have increasingly embraced a hybrid business model over the past decade. This strategy is similar to LCCs based in other parts of the world. The core outcome of this research, the fact that modelling based on artificial intelligence approaches is far more effective than the traditional models prescribed by the International Civil Aviation Organization (ICAO), means that future work is essential to validate this. From an academic perspective, the modelling presented in this study offers considerable promise for future air travel demand forecasting. The results of this thesis provide new insights into LCCs passenger demand forecasting methods and can assist LCCs executives, airports, aviation consultants, and government agencies with a variety of future planning considerations