Article
Business, Finance
Christian M. Dahl, Emma M. Iglesias
Summary: This paper extends previous studies by investigating the tail behavior when a risk premium component is added in the mean equation of different conditional heteroskedastic processes. Three types of parametric models are studied: the traditional GARCH-M model, the double AR model with risk premium, and the GARCH-AR model. The findings suggest that the introduction of an AR process in the mean equation of a traditional GARCH-M process does not impact the tail behavior, but adding a risk premium component in the double AR model changes the tail behavior compared to the GARCH-M model. Additionally, the GARCH-AR model exhibits a different tail index than the traditional AR-GARCH model. Simulation results show that larger tail indexes are associated with the traditional GARCHM model, and the increase in the size of the risk premium component tends to decrease the tail index, except in the case of the double AR model where the risk premium depends on log-volatility. Illustrations and discussions are provided on parameter configurations where the strong stationarity condition of the risk premium models fails.
JOURNAL OF FINANCIAL ECONOMETRICS
(2022)
Article
Mathematics, Applied
Yue Xin, Jinwu Gao, Xiangfeng Yang, Jing Yang
Summary: Uncertain time series analysis is an important component of statistics that utilizes chronological data for forecasting and control. This paper introduces a maximum likelihood estimation method for the uncertain autoregressive moving average model and applies it to analyze range returns in the financial market as well as gold futures and Microsoft stock prices. The results demonstrate the accuracy and robustness of this method.
JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS
(2023)
Article
Engineering, Civil
Mohammad Nazeri Tahroudi, Rasoul Mirabbasi, Yousef Ramezani, Farshad Ahmadi
Summary: This study investigates two efficient approaches for bivariate simulation and compares their applicability in simulating the river discharge in Talezang Basin, Iran. The Copula-GARCH model is found to be more accurate than the optimized SVR model, with increased accuracy at the minimum and maximum values of the data.
WATER RESOURCES MANAGEMENT
(2022)
Article
Materials Science, Multidisciplinary
Jianhua Lu, Ze Li
Summary: The study used the ARIMA model to predict early prognostic factors in patients with AP and established a multifactor mixed forecasting model. Experimental results showed that the creatine kinase index and lactate dehydrogenase index of patients had an impact on prognosis.
RESULTS IN PHYSICS
(2021)
Article
Psychology, Multidisciplinary
Fanshen Han, Chenxi Zhang, Delong Zhu, Fengrui Zhang
Summary: This study combines the methods of discovering and training innovative talents, meeting China's requirements for improving talent training capabilities, and analyzes the relationship between professional enrollments in colleges and universities and the demand for skills in specific areas. By using the SARIMA-BP model and learning from related concepts, the model successfully analyzes and predicts the relationship between professional enrollments and talent demand, revealing a moderate correlation between professional locations and corporate needs.
FRONTIERS IN PSYCHOLOGY
(2022)
Article
Engineering, Chemical
Junhan Huang, Shunli Wang, Wenhua Xu, Weihao Shi, Carlos Fernandez
Summary: This article proposes a new method for predicting the state of health of lithium-ion batteries based on the cycle method and data-driven idea, combining the improved rain flow counting algorithm with the autoregressive integrated moving average model prediction model. Experimental results show that the method has a maximum error of 5.3160%, 5.4517%, and 0.7949% under different conditions.
Article
Automation & Control Systems
Mohammad Reza Mahmoudi, Salman Baroumand
Summary: This study explores the necessity of evaluating the stochastic behavior of sensors by combining SARIMA and PGEE methods and attempts to apply this approach to real load-cell sensor data.
Article
Environmental Sciences
Guangze Liu, Mingkang Yuan, Xudong Chen, Xiaokun Lin, Qingqing Jiang
Summary: Increasing water demand has worsened water shortages in water-scarce regions. Effective water demand forecasting is crucial for sustainable water management. A hybrid ARMA-DNN model was developed in this study and validated using the Minjiang River basin as an example, demonstrating its accuracy in predicting water demand and its potential to alleviate conflicts between water supply and demand.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2023)
Article
Energy & Fuels
Guanghua Zhang, Linghao Zeng, Feng Lian, Xinqiang Liu, Na Fu, Shasha Dai
Summary: This paper addresses the state estimation problem of linear dynamic systems with non-Gaussian noise by introducing a triplet Markov model and deriving a new filter based on correntropy. Simulation results demonstrate the effectiveness of the proposed algorithm.
FRONTIERS IN ENERGY RESEARCH
(2022)
Article
Materials Science, Multidisciplinary
Jing Yuan, Dan Li
Summary: This study aimed to explore the epidemiological distribution characteristics and future development trends of influenza-like illness (ILI) using the autoregressive integrated moving average model (ARIMA) and found that there were significant differences in ILI% among patients of different ages, as well as in the positive rates and subtypes of influenza viruses in different years and seasons. The ARIMA-ENN model showed superior performance in predicting influenza monitoring data compared to the ARIMA model.
RESULTS IN PHYSICS
(2021)
Article
Metallurgy & Metallurgical Engineering
Jagjit Singh, Aldin Ardian, Mustafa Kumral
Summary: The study explored the use of copulas to capture the conditional distribution of factors in mine risk assessment. A multivariate copula-based time-series approach was employed to model uncertain variables, utilizing the ARFIMA-GARCH model for conditional mean and copulas for error distribution to capture collective variation and dependence patterns.
MINING METALLURGY & EXPLORATION
(2021)
Article
Engineering, Electrical & Electronic
Yao Wei, Fengxiang Wang, Hector Young, Dongliang Ke, Jose Rodriguez
Summary: In this article, a model-free predictive current control (MF-PCC) strategy based on the autoregressive moving average (ARMA) structure is proposed and applied to the permanent magnet synchronous motor (PMSM) speed control system to eliminate the influence of parameter mismatches and achieve high model quality. The ARMA model group, consisting of AR, MA, and ARMA structures, is considered to improve model accuracy by accounting for operating states within several sampling periods. The online-designed plant with estimated coefficients using the normalized least-mean-square (NLMS) algorithm achieves improved model quality with reduced calculation burden compared to the recursive least square (RLS) algorithm.
IEEE JOURNAL OF EMERGING AND SELECTED TOPICS IN POWER ELECTRONICS
(2023)
Article
Geosciences, Multidisciplinary
Kezhi Wu, Xin Liu, Xin Jin, Xiaotao Chang, Heping Sun, Jinyun Guo
Summary: A sliding multilayer perceptron (MLP) method combined with singular spectrum analysis (SSA) and autoregressive moving average (ARMA) was proposed for short- and long-term polar motion prediction. The experimental results demonstrated that this method outperformed traditional methods in terms of accuracy.
EARTH PLANETS AND SPACE
(2023)
Article
Public, Environmental & Occupational Health
Yanyan Li, Xingyan Liu, Xinxiao Li, Chenlu Xue, Bingjie Zhang, Yongbin Wang
Summary: This study used Interrupted Time Series (ITS) analysis to assess the impact of COVID-19 outbreak on gonorrhea and predict the epidemic trend. The ITS-ARIMA model outperformed the BSTS model in predicting the gonorrhea incidence trend in China. The findings showed a reduction in gonorrhea cases during the COVID-19 intervention period.
Article
Construction & Building Technology
Hong Yu, Hongping Zhu, Shun Weng, Wangqing Wen, Aiguo Yan, Xingsheng Yu
Summary: This paper proposes a substructural time series model for locating and quantifying the damage in complex systems. A substructural ARMAX model is established to extract the frequencies and mode shapes of substructures as indicators for damage detection. The inverse problem of substructural damage identification is efficiently solved via sparse regularization, and structural damage can be located and quantified through the nonzero terms in the solution vector.
ADVANCES IN STRUCTURAL ENGINEERING
(2023)