A Novel Nonlinear Time Series Prediction Method and its Application in Structural Vibration Response Prediction

2014 ◽  
Vol 599-601 ◽  
pp. 1918-1921 ◽  
Author(s):  
Yi Lin ◽  
Hong Sen Yan ◽  
Bo Zhou
Keyword(s):  
Time Series ◽  
Prediction Accuracy ◽  
Time Series Prediction ◽  
Prediction Method ◽  
Response Prediction ◽  
Nonlinear Time Series ◽  
Vibration Response ◽  
Structural Vibration ◽  
Input Output ◽  
Output Data

A novel nonlinear time series prediction method is proposed in this paper. This prediction method is based on the Multi-dimensional Taylor Network. The structure of the Multi-dimensional Taylor Network is introduced firstly. The Multi-dimensional Taylor Network provides a new method to predict the nonlinear time series. The prediction model based on the Multi-dimensional Taylor Network can realize the prediction of the nonlinear time series just with input-output data without the system mechanism, and it can describe the dynamic characteristics of the system. Finally, the new prediction method is applied in the structural vibration response prediction. Results indicate the validity and the better prediction accuracy of this method.

A Novel Modeling Method Based on Multi-Dimensional Taylor Network and its Application in Time Series Prediction

2014 ◽  
Vol 940 ◽  
pp. 480-484 ◽  
Author(s):  
Yi Lin ◽  
Hong Sen Yan ◽  
Bo Zhou
Keyword(s):  
Time Series ◽  
Prior Knowledge ◽  
Dynamic Characteristics ◽  
Prediction Accuracy ◽  
Time Series Prediction ◽  
Nonlinear Time Series ◽  
Modeling Method ◽  
Input Output ◽  
Output Data ◽  
Bridge Tower

A novel modeling method based on multi-dimensional Taylor network is proposed. The structure and the principle of the multi-dimensional Taylor network are introduced. Based on this, the method is applied in the nonlinear time series prediction based on multi-dimensional Taylor network. It provides a new method to predict the time series, which can describe the dynamic characteristics without prior knowledge and can realize the prediction of the nonlinear time series just with input-output data. An example of predicting the stress data of a large span bridge tower induced by strong typhoon is taken at last in this paper. Results indicate the validity and the better prediction accuracy of this method in nonlinear time series prediction.

A Prediction Method for Nonlinear Correlative Time Series

2010 ◽  
Vol 40-41 ◽  
pp. 930-936 ◽  
Author(s):  
Cong Gui Yuan ◽  
Xin Zheng Zhang ◽  
Shu Qiong Xu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Mutual Information ◽  
Orthogonal Polynomial ◽  
Time Series Prediction ◽  
Prediction Method ◽  
Nonlinear Time Series ◽  
Iterative Learning ◽  
Polynomial Basis

A nonlinear correlative time series prediction method is presented in this paper.It is based on the mutual information of time series and orthogonal polynomial basis neural network. Inputs of network are selected by mutual information, and orthogonal polynomial basis is used as active function.The network is trained by an error iterative learning algorithm.This proposed method for nonlinear time series is tested using two well known time series prediction problems:Gas furnace data time series and Mackey-Glass time series.

A New Prediction Method for Chaotic Time Series

2013 ◽  
Vol 380-384 ◽  
pp. 1673-1676
Author(s):  
Juan Du
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Prediction Accuracy ◽  
Time Series Prediction ◽  
Prediction Method ◽  
Cumulative Effect ◽  
Training Data ◽  
Training Algorithm ◽  
Learning Speed ◽  
Sunspot Data

In order to show the time cumulative effect in the process for the time series prediction, the process neural network is taken. The training algorithm of modified particle swarm is used to the model for the learning speed. The training data is sunspot data from 1700 to 2007. Simulation result shows that the prediction model and algorithm has faster training speed and prediction accuracy than the artificial neural network.

scholarly journals Benchmarking Nonstationary Time Series Prediction

2021 ◽  
Author(s):  
Rebecca Pontes Salles ◽  
Eduardo Ogasawara ◽  
Pedro González
Keyword(s):  
Time Series ◽  
Prediction Accuracy ◽  
Time Series Prediction ◽  
Prediction Method ◽  
Data Transformation ◽  
Nonstationary Time Series ◽  
Prediction Methods ◽  
Simple Task ◽  
Model Training ◽  
Transformation Methods

The prediction of time series has gained increasingly more attention among researchers since it is a crucial aspect of decision-making activities. Unfortunately, most time series prediction methods assume the property of stationarity, i.e., statistical properties do not change over time. In practice, it is the exception and not the rule in most real datasets. Several transformation methods were designed to treat nonstationarity in time series. In this context, nonstationary time series prediction is challenging since it demands knowledge of both data transformation and prediction methods. Since there are no silver bullets, it leads to exploring a large number of data transformation and prediction method combinations for building prediction setups. However, selecting a prediction setup that is appropriate to a particular time series and application is not a simple task. Benchmarking of different candidate combinations helps this selection. This work contributes by providing a review and experimental analysis of transformation methods and a systematic framework (TSPred) for benchmarking and selecting prediction setups for nonstationary time series. Suitable nonstationary time series transformation methods provided improvements of more than 30% in prediction accuracy for half of the evaluated time series. They improved the prediction by more than 95% for 10% of the time series. The features provided by TSPred are also shown to be competitive regarding prediction accuracy. Furthermore, the adoption of a validation phase during model training enables the selection of suitable transformation methods.

scholarly journals Nonlinear time series prediction algorithm based on AD-SSNET for artificial intelligence–powered Internet of Things

2021 ◽  
Vol 17 (3) ◽  
pp. 155014772110041
Author(s):  
Banteng Liu ◽  
Wei Chen ◽  
Meng Han ◽  
Zhangquan Wang ◽  
Ping Sun ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Time Series ◽  
Internet Of Things ◽  
Prediction Accuracy ◽  
Clustering Algorithm ◽  
Time Series Prediction ◽  
Nonlinear Time Series ◽  
Prediction Algorithm ◽  
Subtractive Clustering ◽  
Wireless Internet

Time series have broad usage in the wireless Internet of Things. This article proposes a nonlinear time series prediction algorithm based on the Small-World Scale-Free Network after the AIC-Optimized Subtractive Clustering Algorithm (AIC-DSCA-SSNET, AD-SSNET) to predict the nonlinear and unstable time series, which improves the prediction accuracy. The AD-SSNET is introduced as a reservoir based on the echo state network to improve the predictive capability of nonlinear time series, and combined with artificial intelligence method to construct the prediction model training samples. First, the optimal clustering scheme of randomly distributed neurons in the network is adaptively obtained by the AIC-DSCA, then the AD-SSNET is constructed according to the intra-cluster priority connection algorithm. Finally, the reservoir synaptic matrix is calculated according to the synaptic information. Experimental results show that the proposed nonlinear time series prediction algorithm extends the feasible range of spectral radii of the reservoir, improves the prediction accuracy of nonlinear time series, and has great significance to time series analysis in the era of wireless Internet of Things.

scholarly journals Fault Prediction for Nonlinear System Using Sliding ARMA Combined with Online LS-SVR

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Shengchao Su ◽  
Wei Zhang ◽  
Shuguang Zhao
Keyword(s):  
Time Series ◽  
Nonlinear System ◽  
External Disturbance ◽  
Time Series Prediction ◽  
Prediction Method ◽  
Arma Model ◽  
Estimation Algorithm ◽  
Nonlinear Time Series ◽  
Fault Prediction ◽  
Support Vector

A robust online fault prediction method which combines sliding autoregressive moving average (ARMA) modeling with online least squares support vector regression (LS-SVR) compensation is presented for unknown nonlinear system. At first, we design an online LS-SVR algorithm for nonlinear time series prediction. Based on this, a combined time series prediction method is developed for nonlinear system prediction. The sliding ARMA model is used to approximate the nonlinear time series; meanwhile, the online LS-SVR is added to compensate for the nonlinear modeling error with external disturbance. As a result, the one-step-ahead prediction of the nonlinear time series is achieved and it can be extended ton-step-ahead prediction. The result of then-step-ahead prediction is then used to judge the fault based on an abnormity estimation algorithm only using normal data of system. Accordingly, the online fault prediction is implemented with less amount of calculation. Finally, the proposed method is applied to fault prediction of model-unknown fighter F-16. The experimental results show that the method can predict the fault of nonlinear system not only accurately but also quickly.

AGO-Based Time Series Prediction Method Using LS-SVR

2012 ◽  
Vol 220-223 ◽  
pp. 2133-2137
Author(s):  
Yang Ming Guo ◽  
Xiao Lei Li ◽  
Jie Zhong Ma
Keyword(s):  
Complex System ◽  
Time Series ◽  
Prediction Accuracy ◽  
Time Series Prediction ◽  
Prediction Method ◽  
Health Condition ◽  
Grey System Theory ◽  
Good Prediction ◽  
Grey System ◽  
Complex Dynamic

Fault or health condition prediction of complex system equipments has attracted more and more attention in recent years. Complex system equipments often show complex dynamic behavior and uncertainty, it is difficult to establish precise physical model. Therefore, the time series of complex equipments are often used to implement the prediction in practice. In this paper, in order to improve the prediction accuracy, based on grey system theory, accumulated generating operation (AGO) with raw time series is made to improve the data quality and regularity, and then inverse accumulated generating operation (IAGO) is performed to get the prediction results with the sequence, which is computed by LS-SVR. The results indicate preliminarily that the proposed method is an effective prediction method for its good prediction precision.

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