Passengers Demand Forecasting Based on Chaos Theory

Chaos theory constitutes a promising and powerful tool to address forecasting problems of nonlinear time series, since it catches the dynamical and geometrical structure of very complex systems, ensuring a superior accuracy on the predicted results in comparison to classical approaches, and lowering the complexity typical of the deep learning ones. This paper applies the nonlinear chaos theory principles to the passenger demand forecasting problem. The proposed scheme processes and analyzes the big data of passengers requests collected during one month in the city of Chengdu, China. In order to predict passenger demand behavior, the chaotic trend of the time series has been identified through the largest Lyapunov exponent research. Then, the phase space reconstruction has been pursued, through the detection of the suitable embedding dimension and time delay, to improve forecasting accuracy and avoid information redundancy. A combined local and global predictive method has been proposed, and the validity of the approach is confirmed by comparison with two state-of-art forecasting methods, the auto-regressive and the auto-regressive moving-average with exogenous input. System performance is evaluated in terms of mean squared error and mean percent forecasting error.