scholarly journals Change Point Test for the Conditional Mean of Time Series of Counts Based on Support Vector Regression

Entropy ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. 433
Author(s):  
Sangyeol Lee ◽  
Sangjo Lee
Keyword(s):  
Time Series ◽  
Support Vector Regression ◽  
Stock Prices ◽  
Change Point ◽  
Real Data ◽  
Support Vector ◽  
Time Series Of Counts ◽  
Conditional Mean ◽  
Change Point Test ◽  
Scope Of Application

This study considers support vector regression (SVR) and twin SVR (TSVR) for the time series of counts, wherein the hyper parameters are tuned using the particle swarm optimization (PSO) method. For prediction, we employ the framework of integer-valued generalized autoregressive conditional heteroskedasticity (INGARCH) models. As an application, we consider change point problems, using the cumulative sum (CUSUM) test based on the residuals obtained from the PSO-SVR and PSO-TSVR methods. We conduct Monte Carlo simulation experiments to illustrate the methods’ validity with various linear and nonlinear INGARCH models. Subsequently, a real data analysis, with the return times of extreme events constructed based on the daily log-returns of Goldman Sachs stock prices, is conducted to exhibit its scope of application.

scholarly journals Hybrid CUSUM Change Point Test for Time Series with Time-Varying Volatilities Based on Support Vector Regression

Entropy ◽  
2020 ◽  
Vol 22 (5) ◽  
pp. 578
Author(s):  
Sangyeol Lee ◽  
Chang Kyeom Kim ◽  
Sangjo Lee
Keyword(s):  
Time Series ◽  
Support Vector Regression ◽  
Stock Price ◽  
Real Data ◽  
Support Vector ◽  
Garch Models ◽  
Time Varying ◽  
Tuning Parameters ◽  
Scope Of Application ◽  
Validation Set

This study considers the problem of detecting a change in the conditional variance of time series with time-varying volatilities based on the cumulative sum (CUSUM) of squares test using the residuals from support vector regression (SVR)-generalized autoregressive conditional heteroscedastic (GARCH) models. To compute the residuals, we first fit SVR-GARCH models with different tuning parameters utilizing a time series of training set. We then obtain the best SVR-GARCH model with the optimal tuning parameters via a time series of the validation set. Subsequently, based on the selected model, we obtain the residuals, as well as the estimates of the conditional volatility and employ these to construct the residual CUSUM of squares test. We conduct Monte Carlo simulation experiments to illustrate its validity with various linear and nonlinear GARCH models. A real data analysis with the S&P 500 index, Korea Composite Stock Price Index (KOSPI), and Korean won/U.S. dollar (KRW/USD) exchange rate datasets is provided to exhibit its scope of application.

scholarly journals Predicting Stock Market Index Using Hybrid Intelligence Model

2018 ◽  
Vol 7 (3.15) ◽  
pp. 36 ◽  
Author(s):  
Sarah Nadirah Mohd Johari ◽  
Fairuz Husna Muhamad Farid ◽  
Nur Afifah Enara Binti Nasrudin ◽  
Nur Sarah Liyana Bistamam ◽  
Nur Syamira Syamimi Muhammad Shuhaili
Keyword(s):  
Time Series ◽  
Stock Prices ◽  
Linear Models ◽  
Forecast Error ◽  
Arima Model ◽  
Real Data ◽  
Stock Index ◽  
Support Vector ◽  
Ann Model ◽  
Stock Market Index

Predicting financial market changes is an important issue in time series analysis, receiving an increasing attention due to financial crisis. Autoregressive integrated moving average (ARIMA) model has been one of the most widely used linear models in time series forecasting but ARIMA model cannot capture nonlinear patterns easily. Generalized autoregressive conditional heteroscedasticity (GARCH) model applied understanding of volatility depending to the estimation of previous forecast error and current volatility, improving ARIMA model. Support vector machine (SVM) and artificial neural network (ANN) have been successfully applied in solving nonlinear regression estimation problems. This study proposes hybrid methodology that exploits unique strength of GARCH + SVM model, and GARCH + ANN model in forecasting stock index. Real data sets of stock prices FTSE Bursa Malaysia KLCI were used to examine the forecasting accuracy of the proposed model. The results shows that the proposed hybrid model achieves best forecasting compared to other model.  

Hybrid change point detection for time series via support vector regression and CUSUM method

2020 ◽  
Vol 89 ◽  
pp. 106101 ◽  
Author(s):  
Sangyeol Lee ◽  
Sangjo Lee ◽  
Miteum Moon
Keyword(s):  
Time Series ◽  
Support Vector Regression ◽  
Change Point ◽  
Change Point Detection ◽  
Support Vector ◽  
Cusum Method ◽  
Point Detection

scholarly journals Monitoring Volatility Change for Time Series Based on Support Vector Regression

Entropy ◽  
2020 ◽  
Vol 22 (11) ◽  
pp. 1312
Author(s):  
Sangyeol Lee ◽  
Chang Kyeom Kim ◽  
Dongwuk Kim
Keyword(s):  
Time Series ◽  
Support Vector Regression ◽  
Stock Price ◽  
Financial Time Series ◽  
Real Data ◽  
Support Vector ◽  
Tuning Parameters ◽  
Monitoring Process ◽  
Microsoft Corporation ◽  
Cusum Method

This paper considers monitoring an anomaly from sequentially observed time series with heteroscedastic conditional volatilities based on the cumulative sum (CUSUM) method combined with support vector regression (SVR). The proposed online monitoring process is designed to detect a significant change in volatility of financial time series. The tuning parameters are optimally chosen using particle swarm optimization (PSO). We conduct Monte Carlo simulation experiments to illustrate the validity of the proposed method. A real data analysis with the S&P 500 index, Korea Composite Stock Price Index (KOSPI), and the stock price of Microsoft Corporation is presented to demonstrate the versatility of our model.

Associating stock prices with web financial information time series based on support vector regression

2013 ◽  
Vol 115 ◽  
pp. 142-149 ◽  
Author(s):  
Xun Liang ◽  
Rong-Chang Chen ◽  
Yangbo He ◽  
Ying Chen
Keyword(s):  
Time Series ◽  
Support Vector Regression ◽  
Stock Prices ◽  
Financial Information ◽  
Support Vector ◽  
Information Time

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.

Application of Support Vector Regression for Modeling Low Flow Time Series

2018 ◽  
Vol 23 (2) ◽  
pp. 923-934 ◽  
Author(s):  
Bibhuti Bhusan Sahoo ◽  
Ramakar Jha ◽  
Anshuman Singh ◽  
Deepak Kumar
Keyword(s):  
Time Series ◽  
Support Vector Regression ◽  
Flow Time ◽  
Low Flow ◽  
Support Vector

scholarly journals Fuzzy Semiparametric Support Vector Regression for Seasonal Time Series Analysis

2009 ◽  
Vol 16 (2) ◽  
pp. 335-348
Author(s):  
Joo-Yong Shim ◽  
Chang-Ha Hwang ◽  
Dug-Hun Hong
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Support Vector Regression ◽  
Support Vector ◽  
Series Analysis ◽  
Seasonal Time Series
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