Journal
CHAOS
Volume 30, Issue 2, Pages -Publisher
AIP Publishing
DOI: 10.1063/1.5120502
Keywords
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Categories
Funding
- National Science Foundation of China (NSFC) [61902232]
- Natural Science Foundation of Guangdong Province [2018A030313291, 2018A030313889]
- Education Science Planning Project of Guangdong Province [2018GXJK048]
- STU Scientific Research Foundation for Talents [NTF18006]
- Guangdong Special Cultivation Funds for College Students' Scientific and Technological Innovation [pdjh2020b0222]
- Hong Kong Polytechnic University [1ZE8J]
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Accurate and timely short-term traffic flow forecasting plays a key role in intelligent transportation systems, especially for prospective traffic control. For the past decade, a series of methods have been developed for short-term traffic flow forecasting. However, due to the intrinsic stochastic and evolutionary trend, accurate forecasting remains challenging. In this paper, we propose a noise-immune long short-term memory (NiLSTM) network for short-term traffic flow forecasting, which embeds a noise-immune loss function deduced by maximum correntropy into the long short-term memory (LSTM) network. Different from the conventional LSTM network equipped with the mean square error loss, the maximum correntropy induced loss is a local similar metric, which is immunized to non-Gaussian noises. Extensive experiments on four benchmark datasets demonstrate the superior performance of our NiLSTM network by comparing it with the frequently used models and state-of-the-art models.
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