Nonlinear time series analysis
Nonlinear time series analysis
Chaos and Time-Series Analysis
Chaos and Time-Series Analysis
δ-NARMA neural networks: a new approach to signal prediction
IEEE Transactions on Signal Processing
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This article proposes three novel methods-temporal confined (TC), spatiotemporal confined (STC) and spatial confined (SC)-to forecast the temporal evolution of traffic parameters. The fundamental rationales are to embed one-dimensional traffic time series into reconstructed state spaces and then to perform fuzzy reasoning to infer the future changes in traffic series. The TC, STC and SC methods respectively employ different fuzzy reasoning logics to select similar historical traffic trajectories. Theil inequality coefficient and its decomposed components are used to evaluate the predicting power and source of errors. Field observed one-minute traffic counts are used to test the predicting power. The results show that overall prediction accuracies for the three methods are satisfactorily high with small systematic errors and little deviation from the observed data. It suggests that the proposed three methods can be used to capture and forecast the short-term (e.g., one-minute) temporal evolution of traffic parameters.