Author: Wang, Liya; Mykityshyn, Amy; Johnson, Craig; Marple, Benjamin D.
Title: Deep Learning for Flight Demand and Delays Forecasting Cord-id: i3tdmqhj Document date: 2020_11_6
ID: i3tdmqhj
Snippet: The last few years have seen an increased interest in deep learning (DL) due to its success in applications such as computer vision, natural language processing (NLP), and self-driving cars. Inspired by this success, this paper applied DL to predict flight demand and delays, which have been a concern for airlines and the other stakeholders in the National Airspace System (NAS). Demand and delay prediction can be formulated as a supervised learning problem, where, given an understanding of past h
Document: The last few years have seen an increased interest in deep learning (DL) due to its success in applications such as computer vision, natural language processing (NLP), and self-driving cars. Inspired by this success, this paper applied DL to predict flight demand and delays, which have been a concern for airlines and the other stakeholders in the National Airspace System (NAS). Demand and delay prediction can be formulated as a supervised learning problem, where, given an understanding of past historical demand and delays, a deep learning network can examine sequences of historic data to predict current and future sequences. With that in mind, we applied a well-known DL method, sequence to sequence (seq2seq), to solve the problem. Our results show that the seq2seq method can reduce demand prediction mean squared error (MSE) by 50%, compared to two classical baseline algorithms.
Search related documents:
Co phrase search for related documents- absolute error and lr linear regression: 1, 2, 3
- absolute error and lstm short term memory: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23
- absolute error and machine learning: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58
- absolute error and mae absolute error: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73
- absolute error and mae absolute error mean: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73
- adapt learn ability and machine learning: 1
- adapt learn and machine learning: 1
- additional research and machine learning: 1, 2, 3, 4, 5
- additional research recommend and machine learning: 1
- long lstm short term memory and lr linear regression: 1, 2
- long lstm short term memory and lstm short term memory: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73
- long lstm short term memory and machine learning: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46
- long lstm short term memory and machine translation: 1
- long lstm short term memory and mae absolute error: 1, 2, 3, 4, 5, 6, 7, 8
- long lstm short term memory and mae absolute error mean: 1, 2, 3, 4, 5, 6, 7, 8
- long sequence and lstm short term memory: 1, 2, 3, 4, 5, 6, 7
- long sequence and machine learning: 1, 2, 3, 4, 5
Co phrase search for related documents, hyperlinks ordered by date