A noise immune LSTM network for short term traffic flow forecasting.
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.
Chaos