Track Irregularity Time Series Analysis and Trend Forecasting

The combination of linear and nonlinear methods is widely used in the prediction of time series data. This paper analyzes track irregularity time series data by using gray incidence degree models and methods of data transformation, trying to find the connotative relationship between the time series data. In this paper, GM is based on first-order, single variable linear differential equations; after an adaptive improvement and error correction, it is used to predict the long-term changing trend of track irregularity at a fixed measuring point; the stochastic linear AR, Kalman filtering model, and artificial neural network model are applied to predict the short-term changing trend of track irregularity at unit section. Both long-term and short-term changes prove that the model is effective and can achieve the expected accuracy.

[1]  Andriy Norets,et al.  Estimation of Dynamic Discrete Choice Models Using Artificial Neural Network Approximations , 2012 .

[2]  Jie Ding,et al.  Time series AR modeling with missing observations based on the polynomial transformation , 2010, Math. Comput. Model..

[3]  J. Stock,et al.  A Comparison of Direct and Iterated Multistep Ar Methods for Forecasting Macroeconomic Time Series , 2005 .

[4]  Mehdi Khashei,et al.  A new hybrid artificial neural networks and fuzzy regression model for time series forecasting , 2008, Fuzzy Sets Syst..

[5]  Mehdi Khashei,et al.  An artificial neural network (p, d, q) model for timeseries forecasting , 2010, Expert Syst. Appl..

[6]  Jianguo Yu,et al.  Gray correlation analysis and prediction models of living refuse generation in Shanghai city. , 2007, Waste management.

[7]  T. Senjyu,et al.  Notice of Violation of IEEE Publication PrinciplesA Hybrid ARIMA and Neural Network Model for Short-Term Price Forecasting in Deregulated Market , 2010, IEEE Transactions on Power Systems.

[8]  Mehdi Khashei,et al.  Improvement of Auto-Regressive Integrated Moving Average models using Fuzzy logic and Artificial Neural Networks (ANNs) , 2009, Neurocomputing.

[9]  Çagdas Hakan Aladag,et al.  Forecasting nonlinear time series with a hybrid methodology , 2009, Appl. Math. Lett..

[10]  T. Başar,et al.  A New Approach to Linear Filtering and Prediction Problems , 2001 .

[11]  Bo Wang,et al.  Using near-infrared process analysis to study gas–solid adsorption process as well as its data treatment based on artificial neural network and partial least squares , 2011 .

[12]  Mohamed Khayet,et al.  Artificial neural network modeling and response surface methodology of desalination by reverse osmosis , 2011 .

[13]  Wuhong Wang,et al.  A safety-based approaching behavioural model with various driving characteristics , 2011 .

[14]  Daniel D. Joseph,et al.  Mathematical theory and applications , 1993 .

[15]  Pedro Paulo Balestrassi,et al.  Design of experiments on neural network's training for nonlinear time series forecasting , 2009, Neurocomputing.

[16]  Bjarne A. Foss,et al.  Applying the unscented Kalman filter for nonlinear state estimation , 2008 .

[17]  János Abonyi,et al.  Monitoring process transitions by Kalman filtering and time-series segmentation , 2005, Comput. Chem. Eng..

[18]  Guoqiang Peter Zhang,et al.  Time series forecasting using a hybrid ARIMA and neural network model , 2003, Neurocomputing.

[19]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[20]  Hui Liu,et al.  Comparison of two new ARIMA-ANN and ARIMA-Kalman hybrid methods for wind speed prediction , 2012 .