An ensemble learning based approach for building airfare forecast service

Modern airlines use sophisticated pricing models to maximize their revenue, which results in highly volatile airfares. Without sufficient information, it is usually difficult for ordinary customers to estimate future price changes. Over the last few years, several studies have tried to solve the problem of optimal purchase timing for flight tickets, in which the prediction task is described as a binary classification concerning to buy or wait at a given point. However, forecasting the real-time price changes has never received much attention from the research community. In this paper, we address the problem of airfare forecast and present a systematic approach that covers the most important aspects of building a forecast service, including data modelling, forecast algorithm and long-term prediction strategies. A novel matrix-like data schema is first introduced to organize price series and extract temporal features. For the prediction task, we specifically investigate Learn++.NSE, an incremental ensemble classifier designed for learning in nonstationary environments. We propose a modification of the original algorithm to make a regressor that is capable of learning incrementally from streaming price series, with an extra ability of multi-step ahead forecasting. We further evaluate the forecast model on real-world price data collected from diverse routes and discuss its performance with respect to short-term and long-term prediction.