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TSE-Tran:prediction method of telecommunication-network fraud crime based on time series representation and transformer

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Journal of Safety Science and Resilience 2023年第4期4卷 340-347页

作者： Tuo Shi Jie Fu Xiaofeng Hu Department of Public Security Management Beijing Police CollegeBeijing 102202China Environmental Security Defense Brigade Beijing MunicipalPublic Security Bureau Miyun BranchBeijing 101599China School of Information Technology and Cyber Security People’s Public Security University of ChinaBeijing 100038China

Telecom network fraud has become the most common and concerning type of crime and is an important public security incident that threatens urban resilience.Therefore,preventing a continuous rise in telecommunications and network fraud will help establish a resilient urban governance system.Undertaking the spatiotemporal forecasting of telecommunications-network fraud trends is of significant importance for aiding public security agencies in proactive crime prevention and implementing targeted anti-fraud campaigns.This study presents a telecommunication network fraudulent crime prediction method called TSE-Tran,which integrates temporal representation and transformer architecture.The time-series data of telecommunication-network fraud occurrences were input into the timesNet module,which maps the sequence data to a more precise feature representation tensor that accounts for both intra-and inter-cycle features.Subsequently,the data are fed into the transformer-encoder module for further encoding,capturing long-range dependencies in the time-series data.Finally,occurrences of future telecommunication network frauds are predicted by a fully connected layer.The results of the study demonstrate that the proposed TSE-Tran method outperforms benchmark methods in terms of prediction accuracy.The results of this study are expected to aid in the prevention and control of telecommunications and network frauds effectively strengthen the resilience of urban development and the ability to respond to public security incidents.

关键词： Urban resilience timesNet Transformer time series prediction Attention mechanism

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A framework based on sparse representation model for time series prediction in smart city

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Frontiers of Computer Science 2021年第1期15卷 99-111页

作者： Zhiyong YU Xiangping ZHENG Fangwan HUANG Wenzhong GUO Lin SUN Zhiwen YU College of Mathematics and Computer Science Fuzhou UniversityFuzhou 350108China Hangzhou Key Laboratory for IoT Technology&Application Zhejiang University City CollegeHangzhou 310015China School of Computer Science Northwestern Polytechnical UniversityXi’an 710072China

Smart city driven by Big Data and Internet of Things(loT)has become a most promising trend of the future.As one important function of smart city,event alert based on time series prediction is faced with the challenge of how to extract and represent discriminative features of sensing knowledge from the massive sequential data generated by IoT devices.In this paper,a framework based on sparse representa-tion model(SRM)for time series prediction is proposed as an efficient approach to tackle this challenge.After dividing the over-complete dictionary into upper and lower parts,the main idea of SRM is to obtain the sparse representation of time series based on the upper part firstly,and then realize the prediction of future values based on the lower part.The choice of different dictionaries has a significant impact on the performance of SRM.This paper focuses on the study of dictionary construction strategy and summarizes eight variants of SRM.Experimental results demonstrate that SRM can deal with different types of time series prediction flexibly and effectively.

关键词： sparse representation smart city time series prediction dictionary construction

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Online Sequential Extreme Learning Machine Based Multilayer Perception with Output Self Feedback for time series prediction

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Journal of Shanghai Jiaotong University(Science) 2013年第3期18卷 366-375页

作者：潘峰赵海波 School of Information Science and Technology Donghua University Institude of Aircraft Equipment Naval Academy of Armament

This study presents a time series prediction model with output self feedback which is implemented based on online sequential extreme learning machine. The output variables derived from multilayer perception can feedback to the network input layer to create a temporal relation between the current node inputs and the lagged node outputs while overcoming the limitation of memory which is a vital part for any time-series prediction application. The model can overcome the static prediction problem with most time series prediction models and can effectively cope with the dynamic properties of time series data. A linear and a nonlinear forecasting algorithms based on online extreme learning machine are proposed to implement the output feedback forecasting model. They are both recursive estimator and have two distinct phases: Predict and Update. The proposed model was tested against different kinds of time series data and the results indicate that the model outperforms the original static model without feedback.

关键词： time series prediction extreme learning machine (ELM) autoregression (AR) online sequential learning ELM (OS-ELM) recurrent neural network (RNN)

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Echo State Network Based on Improved Knowledge Distillation for Edge Intelligence

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Chinese Journal of Electronics 2024年第1期33卷 101-111页

作者： Jian ZHOU Yuwen JIANG Lijie XU Lu ZHAO Fu XIAO College of Computer Nanjing University of Posts and Telecommunications Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks

Echo state network(ESN) as a novel artificial neural network has drawn much attention from time series prediction in edge intelligence. ESN is slightly insufficient in long-term memory, thereby impacting the prediction performance. It suffers from a higher computational overhead when deploying on edge devices. We firstly introduce the knowledge distillation into the reservoir structure optimization, and then propose the echo state network based on improved knowledge distillation(ESN-IKD) for edge intelligence to improve the prediction performance and reduce the computational overhead. The model of ESN-IKD is constructed with the classic ESN as a student network, the long and short-term memory network as a teacher network, and the ESN with double loop reservoir structure as an assistant network. The student network learns the long-term memory capability of the teacher network with the help of the assistant network. The training algorithm of ESN-IKD is proposed to correct the learning direction through the assistant network and eliminate the redundant knowledge through the iterative pruning. It can solve the problems of error learning and redundant learning in the traditional knowledge distillation process. Extensive experimental simulation shows that ESN-IKD has a good time series prediction performance in both long-term and short-term memory, and achieves a lower computational overhead.

关键词： Echo state network Reservoir structure optimization Knowledge distillation Edge intelligence time series prediction

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Exploring reservoir computing:Implementation via double stochastic nanowire networks

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Chinese Physics B 2024年第3期33卷 572-582页

作者：唐健峰夏磊李广隶付军段书凯王丽丹 College of Artificial Intelligence Southwest UniversityChongqing 400715China Brain-inspired Computing & Intelligent Control of Chongqing Key Laboratory Chongqing 400715China National & Local Joint Engineering Laboratory of Intelligent Transmission and Control Technology Chongqing 400715China Chongqing Brain Science Collaborative Innovation Center Chongqing 400715China Key Laboratory of Luminescence Analysis and Molecular Sensing Ministry of EducationSouthwest UniversityChongqing 400715China State Key Laboratory of Intelligent Vehicle Safety Technology Chongqing 400715China

Neuromorphic computing,inspired by the human brain,uses memristor devices for complex tasks.Recent studies show that self-organizing random nanowires can implement neuromorphic information processing,enabling data analysis.This paper presents a model based on these nanowire networks,with an improved conductance variation profile.We suggest using these networks for temporal information processing via a reservoir computing scheme and propose an efficient data encoding method using voltage pulses.The nanowire network layer generates dynamic behaviors for pulse voltages,allowing time series prediction analysis.Our experiment uses a double stochastic nanowire network architecture for processing multiple input signals,outperforming traditional reservoir computing in terms of fewer nodes,enriched dynamics and improved prediction accuracy.Experimental results confirm the high accuracy of this architecture on multiple real-time series datasets,making neuromorphic nanowire networks promising for physical implementation of reservoir computing.

关键词： double-layer stochastic(DS)nanowire network architecture neuromorphic computation nanowire network reservoir computing time series prediction

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Chaotic time series prediction using least squares support vector machines

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Chinese Physics B 2004年第4期13卷 454-458页

作者：叶美盈汪晓东 CollegeofMathematicsandPhysics ZhejiangNormalUniversityJinhua321004China CollegeofInformationScienceandEngineering ZhejiangNormalUniversityJinhua321004China

We propose a new technique of using the least squares support vector machines (LS-SVMs) for making one-step and multi-step prediction of chaotic time series. The LS-SVM achieves higher generalization performance than traditional neural networks and provides an accurate chaotic time series prediction. Unlike neural networks' training that requires nonlinear optimization with the danger of getting stuck into local minima, training LS-SVM is equivalent to solving a set of linear equations. Thus it has fast convergence. The simulation results show that LS-SVM has much better potential in the field of chaotic time series prediction.

关键词： chaotic time series time series prediction support vector machines

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STUDY ON THE prediction METHOD OF LOW-DIMENSION time series THAT ARISE FROM THE INTRINSIC NONLINEAR DYNAMICS

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Applied Mathematics and Mechanics(English Edition) 2001年第5期22卷 501-509页

作者： MA Junhai(马军海) CHEN Yushu(陈予恕) School of Management Tianjin University Tianjin 300072 P R China Department of Mechanics Tianjin University Tianjin 300072 P R China

The prediction methods and its applications of the nonlinear dynamic systems determined from chaotic time series of low-dimension are discussed mainly. Based on the work of the foreign researchers, the chaotic time series in the phase space adopting one kind of nonlinear chaotic model were reconstructed. At first, the model parameters were estimated by using the improved least square method. Then as the precision was satisfied, the optimization method was used to estimate these parameters. At the end by using the obtained chaotic model, the future data of the chaotic time series in the phase space was predicted. Some representative experimental examples were analyzed to testify the models and the algorithms developed in this paper. ne results show that if the algorithms developed here are adopted, the parameters of the corresponding chaotic model will be easily calculated well and true. predictions of chaotic series in phase space make the traditional methods change from outer iteration to interpolations. And if the optimal model rank is chosen, the prediction precision will increase notably. Long term superior predictability of nonlinear chaotic models is proved to be irrational and unreasonable.

关键词： nonlinear chaotic model parameter identification time series prediction

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STUDY ON prediction METHODS FOR DYNAMIC SYSTEMS OF NONLINEAR CHAOTIC time series＊

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Applied Mathematics and Mechanics(English Edition) 2004年第6期25卷 605-611页

作者：马军海陈予恕辛宝贵 Management School Tianjin UniversityTianjin300072P.R.China Tianjin University of Finance and Economics Tianjin300222P.R.China Mechanics Department Tianjin Universitytianjin300072P.R.China Tianjin University of Finance and Economics Tianjin300222P.R.China

The prediction methods for nonlinear dynamic systems which are decided by chaotic time series are mainly studied as well as structures of nonlinear self-related chaotic models and their dimensions. By combining neural networks and wavelet theories, the structures of wavelet transform neural networks were studied and also a wavelet neural networks learning method was given. Based on wavelet networks, a new method for parameter identification was suggested, which can be used selectively to extract different scales of frequency and time in time series in order to realize prediction of tendencies or details of original time series. Through pre-treatment and comparison of results before and after the treatment, several useful conclusions are reached: High accurate identification can be guaranteed by applying wavelet networks to identify parameters of self-related chaotic models and more valid prediction of the chaotic time series including noise can be achieved accordingly.

关键词： nonlinear self-related chaotic model wavelet neural network parameter identification time series prediction

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Rolling Iterative prediction for Correlated Multivariate time series

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国际计算机前沿大会会议论文集 2023年第1期 433-452页

作者： Peng Liu Qiong Han Xiao Yang College of Computer Science and Technology Harbin Engineering UniversityHarbin 150001China

Correlated multivariate time series prediction is an effective tool for discovering the chang rules of temporal data,but it is challenging toﬁnd these rules.Recently,deep learning methods have made it possible to predict high-dimensional and complex multivariate time series data.However,these methods cannot capture or predict potential mutation signals of time series,leading to a lag in data prediction trends and large errors.Moreover,it is difﬁcult to capture dependencies of the data,especially when the data is sparse and the time intervals are large.In this paper,we proposed a prediction approach that leverages both propagation dynamics and deep learning,called Rolling Iterative prediction(RIP).In RIP method,the time-Delay Moving Average(TDMA)is used to carry out maximum likelihood reduction on the raw data,and the propagation dynamics model is applied to obtain the potential propagation parameters data,and dynamic properties of the correlated multivariate time series are clearly established.Long Short-Term Memory(LSTM)is applied to capture the time dependencies of data,and the medium and long-term Rolling Iterative prediction method is established by alternately estimating parameters and predicting time series.Experiments are performed on the data of the Corona Virus Disease 2019(COVID-19)in China,France,and South Korea.Experimental results show that the real distribution of the epidemic data is well restored,the prediction accuracy is better than baseline methods.

关键词： time series prediction Correlated Multivariate time series Trend prediction of Infectious Disease Rolling Circulation

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time-series Embeddings from Language Models: A Tool for Wind Direction Nowcasting

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Journal of Meteorological Research 2024年第3期38卷 558-569页

作者： Décio ALVES Fábio MENDON?A Sheikh Shanawaz MOSTAFA Fernando MORGADO-DIAS University of Madeira Campus Universitário da Penteada 9020-105 Funchal Interactive Technologies Institute/Laboratory for Robotics and Engineering Systems and Agência Regional para o Desenvolvimento da Investiga??o Tecnologia e Inova??o Edif.Madeira Tecnopolo

Wind direction nowcasting is crucial in various sectors, particularly for ensuring aviation operations and safety. In this context, the TELMo(time-series Embeddings from Language Models) model, a sophisticated deep learning architecture, has been introduced in this work for enhanced wind-direction nowcasting. Developed by using three years of data from multiple stations in the complex terrain of an international airport, TELMo incorporates the horizontal u(east–west) andv (north–south) wind components to significantly reduce forecasting errors. On a day with high wind direction variability, TELMo achieved mean absolute error values of 5.66 for 2-min, 10.59 for 10-min, and 14.79 for20-min forecasts, processed within a swift 9-ms/step timeframe. Standard degree-based analysis, in comparison, yielded lower performance, emphasizing the effectiveness of the u and v components. In contrast, a Vanilla neural network, representing a shallow-learning approach, underperformed in all analyses, highlighting the superiority of deep learning methodologies in wind direction nowcasting. TELMo is an efficient model, capable of accurately forecasting wind direction for air traffic operations, with an error less than 20° in 97.49% of the predictions, aligning with recommended international thresholds. This model design enables its applicability across various geographical locations, making it a versatile tool in global aviation meteorology.

关键词： wind nowcasting wind components wind direction time series prediction deep learning

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