Application of Time-Series Model to Forecast Track Irregularity Progress

Using Wavelets and Splines to Forecast Non-Stationary Time Series

 This paper deals with a short term forecasting non-stationary time series using wavelets and splines. Wavelets can decompose the series as the sum of two low and high frequency components. Aminghafari and Poggi (2007) proposed to predict high frequency component by wavelets and extrapolate low frequency component by local polynomial fitting. We propose to forecast non-stationary process u...

The progress mechanism of track geometrical irregularity focusing on hanging sleepers

This topic is very traditional and basically has been concerned for a long time. A lot of sophisticated vehicle/track interaction model and track dynamic deterioration model have been developed so far. The author also developed a track settlement progress model comprising a vehicle/track interaction model and a track settlement law. Hanging sleepers are usually caused more or less by the dyn...

A Comparison Between Time Series, Exponential Smoothing, and Neural Network Methods To Forecast GDP of Iran

Tourism Time Series Forecast

In this chapter four combinations of input features and the feedforward, cascade forward and recurrent architectures are compared for the task of forecast tourism time series. The input features of the ANNs consist in the combination of the previous 12 months, the index time modeled by two nodes used to the year and month and one input with the daily hours of sunshine (insolation duration). The...

Study of Track Irregularity Time Series Calibration and Variation Pattern at Unit Section

Focusing on problems existing in track irregularity time series data quality, this paper first presents abnormal data identification, data offset correction algorithm, local outlier data identification, and noise cancellation algorithms. And then proposes track irregularity time series decomposition and reconstruction through the wavelet decomposition and reconstruction approach. Finally, the p...