Abstract
In this paper, we propose a LSTM-based model to cope with airlines’ needs for daily demand forecasting. For short-term (e.g. one day in advance) forecasting, we followed the traditional horizontal time series. But for long-term (e.g. half a month in advance) forecasting, the horizontal time series is no longer capable of doing this due to the lack of input data. So we came up with a novel vertical time series, which is also our main contribution in this paper. The vertical time series we propose possesses great application value and has big potential for future research. Empirical analysis showed that our LSTM-based model achieved the state-of-the-art prediction accuracy among all the tested models in both time series. Developed on a dataset from airline industry though, our approach can be applied to all sales scenarios where sale data is recorded continuously for a fixed period before the sale closes. So our research has big value for the industry.
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Pan, B., Yuan, D., Sun, W., Liang, C., Li, D. (2018). A Novel LSTM-Based Daily Airline Demand Forecasting Method Using Vertical and Horizontal Time Series. In: Ganji, M., Rashidi, L., Fung, B., Wang, C. (eds) Trends and Applications in Knowledge Discovery and Data Mining. PAKDD 2018. Lecture Notes in Computer Science(), vol 11154. Springer, Cham. https://doi.org/10.1007/978-3-030-04503-6_17
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DOI: https://doi.org/10.1007/978-3-030-04503-6_17
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