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Dominant Skyline Query Processing over multiple time series

Journal of Computer Science & Technology 2013年第4期28卷 625-635页

作者：汪浩王朝坤徐亚军宁苑池 Department of Computer Science and Technology Tsinghua University Tsinghua National Laboratory for Information Science and Technology Key Laboratory for Information System Security Ministry of Education School of Software Tsinghua University

multiple time series （MTS）, which describes an object in multi-dimensions, is based on single time series and has been proved to be useful. In this paper, a new analytical method called α/β-Dominant-Skyline on MTS and a formal definition of the α/β-dominant skyline MTS are given. Also, three algorithms, called NL, BC and MFB, are proposed to address the α/β-dominant skyline queries over MTS. Finally experimental results on both synthetic and real data verify the correctness and effectiveness of the proposed method and algorithms.

关键词： multiple time series α/β-dominant skyline skyline query processing

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Heterogeneous information phase space reconstruction and stability prediction of filling body–surrounding rock combination

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International Journal of Minerals,Metallurgy and Materials 2024年第7期31卷 1500-1511页

作者： Dapeng Chen Shenghua Yin Weiguo Long Rongfu Yan Yufei Zhang Zepeng Yan Leiming Wang Wei Chen School of Civil and Resources Engineering University of Science and Technology of BeijingBeijing 100083China Production Technology Department Longshou MineJinchuan Group Co.Ltd.Jinchang 737100China School of Civil Engineering and Architecture East China Jiaotong UniversityNanchang 330013China

Traditional research believes that the filling body can effectively control stress concentration while ignoring the problems of unknown stability and the complex and changeable stress distribution of the filling body–surrounding rock combination under high-stress *** monitoring data processing methods cannot fully consider the complexity of monitoring objects,the diversity of monitoring methods,and the dynamics of monitoring *** solve this problem,this paper proposes a phase space reconstruction and stability prediction method to process heterogeneous information of backfill–surrounding rock *** three-dimensional monitoring system of a large-area filling body–surrounding rock combination in Longshou Mine was constructed by using drilling stress,multipoint displacement meter,and *** information,such as the stress and displacement of the filling body–surrounding rock combination,was continuously *** with the average mutual information method and the false nearest neighbor point method,the phase space of the heterogeneous information of the filling body–surrounding rock combination was then *** this paper,the distance between the phase point and its nearest point was used as the index evaluation distance to evaluate the stability of the filling body–surrounding rock *** evaluated distances(ED)revealed a high sensitivity to the stability of the filling body–surrounding rock *** new method was then applied to calculate the time series of historically ED for 12 measuring points located at Longshou *** moments of mutation in these time series were at least 3 months ahead of the roadway return *** the ED prediction experiments,the autoregressive integrated moving average model showed a higher prediction accuracy than the deep learning models(long short-term memory and Transformer).Furthermore,the root-mean-square error distribution of the prediction results peaked at 0

关键词： deep mining filling body–surrounding rock combination phase space reconstruction multiple time series stability prediction

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维普期刊数据库

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A Novel Hybrid Model for Gasoline Prices Forecasting Based on Lasso and CNN

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Journal of Social Computing 2022年第3期3卷 206-218页

作者： Hu Yang Xinlu Tian Xin Jin Haijun Wang the School of Information Central University of Finance and EconomicsBeijing 100872China School of Economics Beijing Wuzi UniversityBeijing 101149China

Gasoline is the lifeblood of the national *** forecasting of gasoline prices is difficult because of frequent price fluctuations,its complex nature,diverse influencing factors,and low accuracy of prediction *** studies mainly focus on forecasting gasoline prices in a single region by single time series analysis which ignores the daily price co-movement of different series from multiple *** price co-movement may contain useful information for price forecasting,this paper proposes the LassoCNN ensemble model that combines statistical models and deep neural networks to forecast gasoline *** this model,the Least Absolute Shrinkage and Selection Operator(Lasso)screens and chooses the correlated time series to enhance the performance of forecasting and avoid overfitting,while Convolutional Neural Network(CNN)takes the selected multiple series as its input and then forecasts the gasoline prices in a certain *** results of gasoline prices at the national level and regional levels by using the new method demonstrate that the new approach provides more accurate results for the predictions of gasoline prices than those results generated by alternative ***,the relevant series can enhance the performance of forecasting and help to gain better results.

关键词： gasoline prices forecasting Lasso-CNN multiple time series

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IDENTIFICATION OF MULTIVARIATE ARMA MODELS

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Acta Mathematicae Applicatae Sinica 1996年第3期12卷 241-256页

作者：李贵斌 Department of Probability and Statistics Peking University Beijing China

In this paper, we propose the estimates of orders and parameters in identifiable multivariate ARMA models. These estimates are direct and easy to be calculated, and can be proved to follow LIL (Law of iterated logarit... 详细信息

关键词： VARMA multiple time series estimates of orders and parameters LIL CLT

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