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Ximei Liu,Zahid Latif,Daoqi Xiong,Sehrish Khan Saddozai,Kaif Ul Wara 한국정보처리학회 2019 Journal of information processing systems Vol.15 No.5
Stock price is characterized as being mutable, non-linear and stochastic. These key characteristics are known tohave a direct influence on the stock markets globally. Given that the stock price data often contain both linearand non-linear patterns, no single model can be adequate in modelling and predicting time series data. Theautoregressive integrated moving average (ARIMA) model cannot deal with non-linear relationships, however,it provides an accurate and effective way to process autocorrelation and non-stationary data in time seriesforecasting. On the other hand, the neural network provides an effective prediction of non-linear sequences. Asa result, in this study, we used a hybrid ARIMA and neural network model to forecast the monthly closing priceof the Shanghai composite index and Shenzhen component index.
Shadow Price of the Oil Industry
LIU XiMei,WANG ChangFeng,Shahid Rasheed,Muhammad Nawaz Tunio 보안공학연구지원센터 2016 International Journal of u- and e- Service, Scienc Vol.9 No.12
Oil, hailed as modern industrial blood, is of greater significance to a state, and is closely related to people's lives. Shadow price of the oil industry is a key evaluation parameter for the business of an economy. In this paper, the application of input-output method and linear programming theory has been sought to establish an optimization model for the oil industry and the shadow price of the oil industry has been calculated according to the 2007 China input-output table. The analysis concludes that among the agriculture, the industry, and the tertiary industry, the prices of oil industry products bear maximum influence on the prices of agricultural products.
The Nexus among Globalization, ICT and Economic Growth: An Empirical Analysis
Ximei Liu,Zahid Latif,Daoqi Xiong,Mengke Yang,Shahid Latif,Kaif Ul Wara 한국정보처리학회 2021 Journal of information processing systems Vol.17 No.6
Globalization has integrated the world through interaction among countries and people with the help of information and telecommunication technology (ICT). The rapid mode of globalization has put a new life in ICT and economic sector. The key focus of this study is to examine the nexus among the globalization, ICT and economic growth. This study uses autoregressive distributed lag model (ARDL), vector error correction model (VECM) and econometric method spanning from 1990 to 2015. The empirical result highlights that the globalization stimulates economic growth of a country. In addition, both the internet penetration and the mobile phone usage contribute to the economic growth. Lastly, this article contributes important policy lessons on strengthening the economy by utilizing ICT with the rapid globalization.
Liu, Ximei,Latif, Zahid,Xiong, Daoqi,Saddozai, Sehrish Khan,Wara, Kaif Ul Korea Information Processing Society 2019 Journal of information processing systems Vol.15 No.5
Stock price is characterized as being mutable, non-linear and stochastic. These key characteristics are known to have a direct influence on the stock markets globally. Given that the stock price data often contain both linear and non-linear patterns, no single model can be adequate in modelling and predicting time series data. The autoregressive integrated moving average (ARIMA) model cannot deal with non-linear relationships, however, it provides an accurate and effective way to process autocorrelation and non-stationary data in time series forecasting. On the other hand, the neural network provides an effective prediction of non-linear sequences. As a result, in this study, we used a hybrid ARIMA and neural network model to forecast the monthly closing price of the Shanghai composite index and Shenzhen component index.
Meihang Li,Ximei Liu 제어·로봇·시스템학회 2022 International Journal of Control, Automation, and Vol.20 No.7
The iterative parameter estimation methods for a class of nonlinear systems with interval-varying measurements are studied in this paper. According to the auxiliary model identification idea, an auxiliary model is constructed to estimate the unknown noise-free process outputs, and an interval-varying auxiliary model gradient-based iterative identification algorithm is developed. Furthermore, a particle filter, which uses some discrete random sampling points to approximate the posterior probability density function, is adopted to compute the output estimates. Then an interval-varying particle filtering gradient-based iterative algorithm is derived, and an interval-varying auxiliary model based stochastic gradient (V-AM-SG) algorithm is presented for comparison. The simulation results indicate that the proposed algorithms are effective for identifying the nonlinear systems with interval-varying measurements, and can generate more accurate parameter estimates than the V-AM-SG algorithm.
Meihang Li,Ximei Liu 제어·로봇·시스템학회 2020 International Journal of Control, Automation, and Vol.18 No.6
Maximum likelihood methods are based on the probability and statistics theory, and significant for parameter estimation and system modeling. This paper combines the maximum likelihood principle with the data filtering technique for parameter estimation of a class of bilinear systems. The input-output representation of a bilinear system is derived through eliminating the state variables in the model. Then, a filtering based maximum likelihood iterative least squares algorithm is proposed for identifying the parameters of bilinear systems with colored noises by filtering the input-output data with a filter. A least squares based iterative algorithm is given for comparison. The simulation results indicate that the proposed algorithm is effective for identifying bilinear systems. The filtering based maximum likelihood iterative least squares algorithm is more accurate under different noise variance, and has higher computational efficiency.
Lijuan Wan,Feng Ding,Ximei Liu,Chunping Chen 제어·로봇·시스템학회 2020 International Journal of Control, Automation, and Vol.18 No.3
This paper investigates the identification methods for controlled autoregressive systems with autoregressive noise (i.e., equation-error autoregressive systems) from given input and output data. By applying the iterative technique and the hierarchical identification principle, an iterative least squares identification algorithm is presented and a recursive generalized least squares algorithm is given for comparison. The basic idea is to replace the unknown noise terms in the information vector with their estimated residuals. The simulation test results show the effectiveness of these algorithms.