Displacement Time Series Prediction Model of Landslide Based on Phase Space Reconstruction

2014 ◽  
Vol 1065-1069 ◽  
pp. 23-26
Author(s):  
Ting Yao Jiang ◽  
Shan Shan Wang
Keyword(s):  
Time Series ◽  
Phase Space ◽  
Time Series Prediction ◽  
Phase Space Reconstruction ◽  
Support Vector ◽  
Support Vector Regression Model ◽  
Univariate Time Series ◽  
Higher Dimensional ◽  
Dimensional State Space ◽  
Phase Space Theory

In order to fully reveal information about landslide displacement, it was necessary to extend a time series to a higher-dimensional state space for the characteristic of univariate time series. However, in order to control the expansion of noise, an appropriate embedded dimension of phase space reconstruction was not the bigger the better. In this paper, based on the displacement time series of landslide, the phase space theory was used to build displacement time series matrix and the entropy theory was used to get the entropy. The embedded dimension of phase space reconstruction could be adjusted according to the change of entropy and feedback of displacement prediction error and a support vector regression model was created via the support vector machine’s learning. The application on Baijiabao landslide indicates that the proposed method achieves a high accuracy and stability of prediction.

scholarly journals Prediction of Energy-Efficient Production of Coalbed Methane Based on Chaotic Time Series and Bayes-Least Squares-Support Vector Machine

2020 ◽  
Vol 38 (4) ◽  
pp. 933-940
Author(s):  
Yan Wang ◽  
Zhongshui Man ◽  
Meihua Lu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Support Vector Machine ◽  
Phase Space ◽  
Energy Efficient ◽  
Coalbed Methane ◽  
Phase Space Reconstruction ◽  
Support Vector ◽  
Proposed Model ◽  
Bayesian Evidence

The productivity of coalbed methane (CBM) depends heavily on the heat environment, and directly reflects the quality of the well. Following the theories of phase space reconstruction and Bayesian evidence framework, this paper puts forward a Bayes-least squares-support vector machine (Bayes-LS-SVM) model for the prediction of energy-efficient productivity of CBM under Bayesian evidence network based on chaotic time series. The energy-efficient productivity stands for the gas and water production of CBM wells at a low energy consumption, despite the disturbance from the heat environment. The proposed model avoids the local optimum trap of backpropagation neural network (BPNN), and overcomes the main defects of the SVM: high time consumption of parameter determination, and proneness to overfitting. In our model, the model parameters are optimized through three-layer Bayesian evidence inference, and the input vector for prediction is selected adaptively. In this way, the model construction is not too empirical, and the constructed model is highly adaptive. Then, the theory on phase space reconstruction was applied to investigate the chaotic property of the time series on CBM production, and the Bayes-LS-SVM was adopted to predict the time series after phase space reconstruction, in comparison with neural network prediction methods like SVM and BPNN. Experimental results show that the proposed model boast quick computing, accurate fitting, flexible structure, and strong generalization ability.

scholarly journals Hidden Phase Space Reconstruction: A Novel Chaotic Time Series Prediction Method for Speech Signals

2018 ◽  
Vol 27 (6) ◽  
pp. 1221-1228 ◽  
Author(s):  
Jingjing Li ◽  
Qijin Zhang ◽  
Yumei Zhang ◽  
Xiaojun Wu ◽  
Xiaoming Wang ◽  
...  
Keyword(s):  
Time Series ◽  
Phase Space ◽  
Time Series Prediction ◽  
Prediction Method ◽  
Phase Space Reconstruction ◽  
Chaotic Time Series ◽  
Speech Signals ◽  
Chaotic Time Series Prediction

scholarly journals Fast Univariate Time Series Prediction of Solar Power for Real-Time Control of Energy Storage System

Forecasting ◽  
2018 ◽  
Vol 1 (1) ◽  
pp. 107-120 ◽  
Author(s):  
Mostafa Majidpour ◽  
Hamidreza Nazaripouya ◽  
Peter Chu ◽  
Hemanshu Pota ◽  
Rajit Gadh
Keyword(s):  
Power Generation ◽  
Time Series ◽  
Solar Power ◽  
Time Series Prediction ◽  
Percentage Error ◽  
Acquisition Time ◽  
Support Vector ◽  
Solar Power Generation ◽  
Prediction Algorithms ◽  
Univariate Time Series

In this paper, super-short-term prediction of solar power generation for applications in dynamic control of energy system has been investigated. In order to follow and satisfy the dynamics of the controller, the deployed prediction method should have a fast response time. To this end, this paper proposes fast prediction methods to provide the control system with one step ahead of solar power generation. The proposed methods are based on univariate time series prediction. That is, instead of using external data such as the weather forecast as the input of prediction algorithms, they solely rely on past values of solar power data, hence lowering the volume and acquisition time of input data. In addition, the selected algorithms are able to generate the forecast output in less than a second. The proposed methods in this paper are grounded on four well-known prediction algorithms including Autoregressive Integrated Moving Average (ARIMA), K-Nearest Neighbors (kNN), Support Vector Regression (SVR), and Random Forest (RF). The speed and accuracy of the proposed algorithms have been compared based on two different error measures, Mean Absolute Error (MAE) and Symmetric Mean Absolute Percentage Error (SMAPE). Real world data collected from the PV installation at the University of California, Riverside (UCR) are used for prediction purposes. The results show that kNN and RF have better predicting performance with respect to SMAPE and MAE criteria.

scholarly journals Phase Space Reconstruction-Based Conceptor Network for Time Series Prediction

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 163172-163179 ◽  
Author(s):  
Zheng Xu ◽  
Ling Zhong ◽  
Anguo Zhang
Keyword(s):  
Time Series ◽  
Phase Space ◽  
Time Series Prediction ◽  
Phase Space Reconstruction
Sign in / Sign up

Export Citation Format

Share Document