scholarly journals Coercively Adjusted Auto Regression Model for Forecasting in Epilepsy EEG

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Sun-Hee Kim ◽  
Christos Faloutsos ◽  
Hyung-Jeong Yang
Keyword(s):  
Time Series ◽  
Fractal Dimension ◽  
Regression Model ◽  
Random Coefficients ◽  
Experimental Results ◽  
Ar Model ◽  
Forecasting Method ◽  
Eeg Data ◽  
Future Value ◽  
Auto Regression

Recently, data with complex characteristics such as epilepsy electroencephalography (EEG) time series has emerged. Epilepsy EEG data has special characteristics including nonlinearity, nonnormality, and nonperiodicity. Therefore, it is important to find a suitable forecasting method that covers these special characteristics. In this paper, we propose a coercively adjusted autoregression (CA-AR) method that forecasts future values from a multivariable epilepsy EEG time series. We use the technique of random coefficients, which forcefully adjusts the coefficients with−1and 1. The fractal dimension is used to determine the order of the CA-AR model. We applied the CA-AR method reflecting special characteristics of data to forecast the future value of epilepsy EEG data. Experimental results show that when compared to previous methods, the proposed method can forecast faster and accurately.

Using the AR–SVR–CPSO hybrid model to forecast vibration signals in a high-speed train transmission system

2018 ◽  
Vol 233 (7) ◽  
pp. 701-714
Author(s):  
Yumei Liu ◽  
Ningguo Qiao ◽  
Congcong Zhao ◽  
Jiaojiao Zhuang ◽  
Guangdong Tian
Keyword(s):  
Time Series ◽  
Regression Model ◽  
Support Vector Regression ◽  
Hybrid Model ◽  
High Speed ◽  
Support Vector ◽  
High Speed Train ◽  
Support Vector Regression Model ◽  
Vibration Time ◽  
Auto Regression

Accurate vibration time series modeling can mine the internal law of data and provide valuable references for reliability assessment. To improve the prediction accuracy, this study proposes a hybrid model – called the AR–SVR–CPSO hybrid model – that combines the auto regression (AR) and support vector regression (SVR) models, with the weights optimized by the chaotic particle swarm optimization (CPSO) algorithm. First, the auto regression model with the difference method is employed to model the vibration time series. Second, the support vector regression model with the phase space reconstruction is constructed for predicting the vibration time series once more. Finally, the predictions of the AR and SVR models are weighted and summed together, with the weights being optimized by the CPSO. In addition, the data collected from the reliability test platform of high-speed train transmission systems and the “NASA prognostics data repository” are used to validate the hybrid model. The experimental results demonstrate that the hybrid model proposed in this study outperforms the traditional AR and SVR models.

Time Series Analysis of Ultrasonic Observations of Gross Fetal Body Movements during the Last 10 Weeks of Pregnancy

1981 ◽  
Vol 3 (4) ◽  
pp. 330-341 ◽  
Author(s):  
Karen Campbell ◽  
Ian MacNeill ◽  
John Patrick
Keyword(s):  
Time Series ◽  
Spectral Analysis ◽  
Time Series Analysis ◽  
Regression Model ◽  
Body Movement ◽  
Frequency Range ◽  
Body Movements ◽  
Significant Power ◽  
First Order ◽  
Auto Regression

Thirty fetuses were observed for 24 hours and one fetus was observed for 20 hours during the last 10 weeks of gestation. Observations were made of the amount of gross fetal body movement in each successive 5 minute observation epoch, thus resulting in 30 time series of 288 observations and one time series of 240 observations. Spectral analysis of these time series demonstrated the presence of significant power in the frequency range of 0.002 to 0.0175 cpm. Application of Box-Jenkins techniques to the time series resulted in the choice of a first-order auto-regression model to fit the data. It was concluded that the incidence of episodes of gross fetal body movements were non-random and were, in fact, pseudoperiodic.

scholarly journals Signal Identification of Gear Vibration in Engine-Gearbox Systems Based on Auto-Regression and Optimized Resonance-Based Signal Sparse Decomposition

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1868
Author(s):  
Yuanyuan Huang ◽  
Shuiguang Tong ◽  
Zheming Tong ◽  
Feiyun Cong
Keyword(s):  
Genetic Algorithm ◽  
Online Monitoring ◽  
Mechanical Systems ◽  
Vibration Signal ◽  
Experimental Results ◽  
Ar Model ◽  
Signal Identification ◽  
Vibration Signals ◽  
Sparse Decomposition ◽  
Auto Regression

As an essential part of the transmission system, gearboxes are considered as a major source of vibration. Signal identification of gear vibration is necessary for online monitoring of the mechanical systems. However, in engine-gearbox systems, the ignition impact of the engine is strong, so that the gear vibration is generally submerged. To overcome this issue, the resonance-based signal sparse decomposition (RSSD) method is used in this paper based on different oscillatory behaviors of the gear meshing impact and the engine ignition impact. To improve the accuracy of RSSD under interferences, the meshing frequency energy ratio (MF–ER) index is introduced into RSSD to adaptively choose the decomposition parameters. Before applying the RSSD method, the auto-regression (AR) model is used as a pre-whitening step to eliminate the normal gear meshing vibration, which improves the decomposition performance of RSSD. The effectiveness of the proposed AR-ORSSD (AR-based optimized RSSD) algorithm is tested using both simulated signals and measured vibration signals from an engine-gearbox system in a forklift. Comparisons were made with the RSSD algorithm based on a genetic algorithm. Experimental results indicate that the AR-ORSSD algorithm is superior at identifying gear vibration signals especially when under strong interferences.

scholarly journals Stock Market Prediction

2020 ◽  
Vol 9 (9) ◽  
pp. 506-510
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Regression Model ◽  
Current System ◽  
Efficient Algorithms ◽  
Time Series Model ◽  
Stock Market Prediction ◽  
Preferred Stock ◽  
Regression Algorithms ◽  
Future Value

The Stock Market is a challenging forum for investment and requires immense brainstorming before one shall put their hard earned money to work. This project aims at processing large volumes of data and running comprehensive regression algorithms on the dataset; that will predict the future value of a stock using the regression model with the highest accuracy. The purpose of this paper is to analyze the shortcomings of the current system and building a time-series model that would mitigate most of them by implementing more efficient algorithms. Using this model, anyone can monitor the preferred stock that they want to invest in; and maximize profit by purchasing volume at the lowest price and liquidating the stock when it’s at its highest.

Alternative Models for Forecasting Macroeconomic Indicators in Kazakhstan

2017 ◽  
Vol 4 (1) ◽  
pp. 1-15
Author(s):  
Saken Pirmakhanov
Keyword(s):  
Time Series ◽  
Regression Model ◽  
Regression Models ◽  
Error Prediction ◽  
Optimal Model ◽  
Short Term ◽  
Macroeconomic Indicators ◽  
Vector Auto Regression ◽  
In Virtue Of ◽  
Auto Regression

This paper indicates special aspects of using vector auto-regression models to forecast rates of basic macroeconomic indicators in short term. In particular, traditional vector auto-regression model, Bayesian vector auto-regression model and factor augmented vector auto-regression model are shown. For parameter estimation of these models the author uses time series of Kazakhstani macroeconomic indicators between 1996 and 2015 quarterly. In virtue of mean-root-square error prediction the conclusion of optimal model is going to be chosen.

Prediction of Power Transformer Fault Based on Auto Regression Model

2011 ◽  
Vol 317-319 ◽  
pp. 2230-2233
Author(s):  
Rui Rui Zheng ◽  
Bao Chun Wu ◽  
Ji Yin Zhao
Keyword(s):  
Regression Model ◽  
Power Transformer ◽  
Information Criterion ◽  
Fault Prediction ◽  
Transformer Oil ◽  
Ar Model ◽  
Prediction Algorithm ◽  
Gray Model ◽  
Transformer Fault ◽  
Auto Regression

Dissolved gases analysis is the essence to diagnose and forecast power transformer fault. This paper utilized an Auto Regression model to predict contents of gases dissolved in power transformer oil, and adopted Akaike's Information Criterion to determine model order. Then, the prediction results of AR model are compared with results of Gray model. Finally, gray artificial immune algorithm diagnosed power transformer fault types through gases contents predicted by Auto Regression model. Experiments demonstrates that Auto Regression model has a higher accuracy than Gray Model, and the fault prediction results of the proposed algorithm are in accord with the results using real gases contents, thus , the power transformer fault prediction algorithm present in the paper is effective and reliable.

scholarly journals Load forecasting of refrigerated display cabinet based on CEEMD–IPSO–LSTM combined model

Open Physics ◽  
2021 ◽  
Vol 19 (1) ◽  
pp. 360-374
Author(s):  
Yuan Pei ◽  
Lei Zhenglin ◽  
Zeng Qinghui ◽  
Wu Yixiao ◽  
Lu Yanli ◽  
...  
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Data ◽  
Load Forecasting ◽  
Series Data ◽  
Forecasting Model ◽  
Combined Model ◽  
Forecasting Accuracy ◽  
Forecasting Method ◽  
Consumption Reduction

Abstract The load of the showcase is a nonlinear and unstable time series data, and the traditional forecasting method is not applicable. Deep learning algorithms are introduced to predict the load of the showcase. Based on the CEEMD–IPSO–LSTM combination algorithm, this paper builds a refrigerated display cabinet load forecasting model. Compared with the forecast results of other models, it finally proves that the CEEMD–IPSO–LSTM model has the highest load forecasting accuracy, and the model’s determination coefficient is 0.9105, which is obviously excellent. Compared with other models, the model constructed in this paper can predict the load of showcases, which can provide a reference for energy saving and consumption reduction of display cabinet.

scholarly journals Using Convolutional Neural Network and Candlestick Representation to Predict Sports Match Outcomes

2021 ◽  
Vol 11 (14) ◽  
pp. 6594
Author(s):  
Yu-Chia Hsu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Pattern Recognition ◽  
Logistic Regression ◽  
Regression Model ◽  
Convolutional Neural Network ◽  
Logistic Regression Model ◽  
National Football League ◽  
Performance Metrics ◽  
Betting Market

The interdisciplinary nature of sports and the presence of various systemic and non-systemic factors introduce challenges in predicting sports match outcomes using a single disciplinary approach. In contrast to previous studies that use sports performance metrics and statistical models, this study is the first to apply a deep learning approach in financial time series modeling to predict sports match outcomes. The proposed approach has two main components: a convolutional neural network (CNN) classifier for implicit pattern recognition and a logistic regression model for match outcome judgment. First, the raw data used in the prediction are derived from the betting market odds and actual scores of each game, which are transformed into sports candlesticks. Second, CNN is used to classify the candlesticks time series on a graphical basis. To this end, the original 1D time series are encoded into 2D matrix images using Gramian angular field and are then fed into the CNN classifier. In this way, the winning probability of each matchup team can be derived based on historically implied behavioral patterns. Third, to further consider the differences between strong and weak teams, the CNN classifier adjusts the probability of winning the match by using the logistic regression model and then makes a final judgment regarding the match outcome. We empirically test this approach using 18,944 National Football League game data spanning 32 years and find that using the individual historical data of each team in the CNN classifier for pattern recognition is better than using the data of all teams. The CNN in conjunction with the logistic regression judgment model outperforms the CNN in conjunction with SVM, Naïve Bayes, Adaboost, J48, and random forest, and its accuracy surpasses that of betting market prediction.

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