Fire Time Series Forecasting Based on Markov-SVR Model

Based on support vector regression and Markovstate transition, a new prediction model termed as Markovsupportvector regression (MSVR) model is proposed toforecast the fire time series. In the proposed model, a SVR is tobuild an optimal prediction model from a series of fire data,and then uses the Markov state transition to reduce theresiduals errors produced by the mentioned model. Theproposed model is examined using actual fire time series data.The results show that the MSVR model gets the better resultperformance than that of the pure SVR model.

[1]  Chin-Teng Lin,et al.  Neural-Network-Based Fuzzy Logic Control and Decision System , 1991, IEEE Trans. Computers.

[2]  Richard L. Tweedie,et al.  Markov Chains and Stochastic Stability , 1993, Communications and Control Engineering Series.

[3]  Kishan G. Mehrotra,et al.  Forecasting the behavior of multivariate time series using neural networks , 1992, Neural Networks.

[4]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[5]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[6]  Mukhtiar Singh.  ADAPTIVE NETWORK-BASED FUZZY INFERENCE SYSTEMS FOR SENSORLESS CONTROL OF PMSG BASED WIND TURBINE WITH POWER QUALITY IMPROVEMENT FEATURES , 2010 .

[7]  William J. Stewart,et al.  Introduction to the numerical solution of Markov Chains , 1994 .

[8]  Jerry M. Mendel,et al.  Generating fuzzy rules by learning from examples , 1992, IEEE Trans. Syst. Man Cybern..

[9]  John Moody,et al.  Fast Learning in Networks of Locally-Tuned Processing Units , 1989, Neural Computation.

[10]  Shun-Feng Su,et al.  Robust support vector regression networks for function approximation with outliers , 2002, IEEE Trans. Neural Networks.

[11]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[12]  ZHANGYou-yun,et al.  A Grey Prediction Model on Vibration Severity Development of a Pump , 2004 .

[13]  Tom Burr.  Comparison of fuzzy forecaster to a statistically motivated forecaster , 1998, IEEE Trans. Syst. Man Cybern. Part A.

[14]  Zhang You-yun.  A Grey Prediction Model on Vibration Severity Development of a Pump , 2004 .

[15]  Chan-Ben Lin,et al.  High-precision forecast using grey models , 2001 .

[16]  Alexander J. Smola,et al.  Support Vector Method for Function Approximation, Regression Estimation and Signal Processing , 1996, NIPS.