scholarly journals Time Series Adaptive Online Prediction Method Combined with Modified LS-SVR and AGO

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
Guo Yangming ◽  
Zhang Lu ◽  
Cai Xiaobin ◽  
Ran Congbao ◽  
Zhai Zhengjun ◽  
...  
Keyword(s):  
Time Series ◽  
Complex Systems ◽  
Incremental Learning ◽  
Learning Algorithm ◽  
Computing Time ◽  
Grey System Theory ◽  
Good Prediction ◽  
Grey System ◽  
Online Prediction ◽  
Prediction Approach

Fault or health condition prediction of the complex systems has attracted more attention in recent years. The complex systems often show complex dynamic behavior and uncertainty, which makes it difficult to establish a precise physical model. Therefore, the time series of complex system is used to implement prediction in practice. Aiming at time series online prediction, we propose a new method to improve the prediction accuracy in this paper, which is based on the grey system theory and incremental learning algorithm. In this method, the accumulated generating operation (AGO) with the raw time series is taken to improve the data quality and regularity firstly; then the prediction is conducted by a modified LS-SVR model, which simplifies the calculation process with incremental learning; finally, the inverse accumulated generating operation (IAGO) is performed to get the prediction results. The results of the prediction experiments indicate preliminarily that the proposed scheme is an effective prediction approach for its good prediction precision and less computing time. The method will be useful in actual application.

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.

Grey system theory-based models in time series prediction

2010 ◽  
Vol 37 (2) ◽  
pp. 1784-1789 ◽  
Author(s):  
Erdal Kayacan ◽  
Baris Ulutas ◽  
Okyay Kaynak
Keyword(s):  
System Theory ◽  
Time Series ◽  
Time Series Prediction ◽  
Grey System Theory ◽  
Grey System

scholarly journals The Development of a Hybrid Wavelet-ARIMA-LSTM Model for Precipitation Amounts and Drought Analysis

Atmosphere ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 74
Author(s):  
Xianghua Wu ◽  
Jieqin Zhou ◽  
Huaying Yu ◽  
Duanyang Liu ◽  
Kang Xie ◽  
...  
Keyword(s):  
Time Series ◽  
Short Term Memory ◽  
Moving Average ◽  
Grey System Theory ◽  
Small Sample ◽  
Annual Precipitation ◽  
Drought Event ◽  
Monthly Precipitation ◽  
Grey Theory ◽  
Grey System

Investigation of quantitative predictions of precipitation amounts and forecasts of drought events are conducive to facilitating early drought warnings. However, there has been limited research into or modern statistical analyses of precipitation and drought over Northeast China, one of the most important grain production regions. Therefore, a case study at three meteorological sites which represent three different climate types was explored, and we used time series analysis of monthly precipitation and the grey theory methods for annual precipitation during 1967–2017. Wavelet transformation (WT), autoregressive integrated moving average (ARIMA) and long short-term memory (LSTM) methods were utilized to depict the time series, and a new hybrid model wavelet-ARIMA-LSTM (W-AL) of monthly precipitation time series was developed. In addition, GM (1, 1) and DGM (1, 1) of the China Z-Index (CZI) based on annual precipitation were introduced to forecast drought events, because grey system theory specializes in a small sample and results in poor information. The results revealed that (1) W-AL exhibited higher prediction accuracy in monthly precipitation forecasting than ARIMA and LSTM; (2) CZI values calculated through annual precipitation suggested that more slight drought events occurred in Changchun while moderate drought occurred more frequently in Linjiang and Qian Gorlos; (3) GM (1, 1) performed better than DGM (1, 1) in drought event forecasting.

scholarly journals Multi-Time Resolution Ensemble LSTMs for Enhanced Feature Extraction in High-Rate Time Series

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1954
Author(s):  
Vahid Barzegar ◽  
Simon Laflamme ◽  
Chao Hu ◽  
Jacob Dodson
Keyword(s):  
Time Series ◽  
Feature Extraction ◽  
Short Term Memory ◽  
Learning Algorithm ◽  
Computing Time ◽  
Heuristic Method ◽  
High Rate ◽  
Stationary Time Series ◽  
Rate System ◽  
Modeling Tools

Systems experiencing high-rate dynamic events, termed high-rate systems, typically undergo accelerations of amplitudes higher than 100 g-force in less than 10 ms. Examples include adaptive airbag deployment systems, hypersonic vehicles, and active blast mitigation systems. Given their critical functions, accurate and fast modeling tools are necessary for ensuring the target performance. However, the unique characteristics of these systems, which consist of (1) large uncertainties in the external loads, (2) high levels of non-stationarities and heavy disturbances, and (3) unmodeled dynamics generated from changes in system configurations, in combination with the fast-changing environments, limit the applicability of physical modeling tools. In this paper, a deep learning algorithm is used to model high-rate systems and predict their response measurements. It consists of an ensemble of short-sequence long short-term memory (LSTM) cells which are concurrently trained. To empower multi-step ahead predictions, a multi-rate sampler is designed to individually select the input space of each LSTM cell based on local dynamics extracted using the embedding theorem. The proposed algorithm is validated on experimental data obtained from a high-rate system. Results showed that the use of the multi-rate sampler yields better feature extraction from non-stationary time series compared with a more heuristic method, resulting in significant improvement in step ahead prediction accuracy and horizon. The lean and efficient architecture of the algorithm results in an average computing time of 25 μμs, which is below the maximum prediction horizon, therefore demonstrating the algorithm’s promise in real-time high-rate applications.

scholarly journals Study on the performance evaluation and prediction model of self-compacting concrete in steel shell immersed tube

2021 ◽  
Vol 248 ◽  
pp. 03075
Author(s):  
Song Shenyou ◽  
Yu Fang ◽  
Chen Wenguang ◽  
Xu Jinjun ◽  
Zhao Jiaqi ◽  
...  
Keyword(s):  
Prediction Models ◽  
Grey System Theory ◽  
Steel Shell ◽  
Support Vector ◽  
Good Prediction ◽  
Grey System ◽  
Self Compacting Concrete ◽  
Good Prediction Accuracy ◽  
Sample Test ◽  
Target Performance

In order to study the quality control and evaluation methods of self-compacting concrete (SCC) pumping process in Shenzhen-Zhongshan Bridge and similar projects, sample test is performed on self-compacting concrete mixture collected from the pumping field of the E1-E4 steel shell immersed tube; Then a database base on relationship between the variation parameters and the target performance is established. On this basis, the Grey system theory is adopted to analyze the parameter sensitivity of the SCC pumping performance to the different kinds of variables. The results show that variables are related to target performance and some of the variables have a significant influence. Using the powerful data mining capability of support-vector machine and Bayesian statistical inference in the case of uncertain exact mathematical relationship between independent variables and dependent variables, implicit and explicit prediction models of variation of SCC pumping performance are respectively established by pumping distance, number of elbows, pumping time and environmental temperature as the control parameters. Finally, the comparisons between the measured data and calculation result prove that both models have good prediction accuracy and stability.

Deformation Prediction Based on Time Series Analysis and Grey System Theory

2011 ◽  
Vol 368-373 ◽  
pp. 2147-2152 ◽  
Author(s):  
Dong Liang Qiao ◽  
Ming Zhao
Keyword(s):  
System Theory ◽  
Time Series ◽  
Time Series Analysis ◽  
Large Deviation ◽  
Original Data ◽  
Grey System Theory ◽  
Grey System ◽  
Deformation Prediction ◽  
Series Analysis

For the long-term monitoring of structure, the deformation trend changes periodically and is hard to extract. A small amount of recent data can be selected to avoid such problem. The study refers to the idea of grey system theory and provides an improved way of deformation prediction in time series analysis with a small amount of data. By cumulating the original data, the trend item is made clear and the rule of data becomes obvious. The prediction results show that the way provided by this article gives a more accurate prediction in the short term. When the prediction results have a large deviation with actual deformation, it can be believed that the trend has changed and the monitored structure may be affected.

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