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Time series forecasting is an important aspect of dynamic data analysis and processing, in science, economics, engineering and many other applications there exists using the historical data to predict the problem of the future, and is one considerable practical value of applied research. Time series forecasting is an interdisciplinary study field, this paper is under the guidance of the introduction...
According to the floating-car data measured from urban links, some data-processing techniques including data mending, wavelet de-noise and others are used to establish a time series of data to better reflect the original running characteristic of urban links. On this basis, the travel time forecasting researches are executed both by the BP neural network based on Bayesian Regularization algorithm...
Stock market forecasting has attracted a lot of research interests in previous literature. Traditionally, the autoregressive moving average (ARMA) model has been one of the most widely used linear models in time series forecasting. However, the ARMA model cannot easily capture the nonlinear patterns. And recent studies have shown that artificial neural networks (ANN) method achieved better performance...
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