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Stock Price Forecasting: An echo state network Approach

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Computer Systems Science & Engineering 2021年第3期36卷 509-520页

作者： Guang Sun Jingjing Lin Chen Yang Xiangyang Yin Ziyu Li Peng Guo Junqi Sun Xiaoping Fan Bin Pan Hunan University of Finance and Economics ChangshaChina University Malaysia Sabah Kota KinabaluMalaysia Yali High School International Department ChangshaChina

Forecasting stock prices using deep learning models suffers from pro-blems such as low accuracy,slow convergence,and complex network structures.This study developed an echo state network(ESN)model to mitigate such pro-blems.We compared our ESN with a long short-term memory(LSTM)network by forecasting the stock data of Kweichow Moutai,a leading enterprise in China’s liquor industry.By analyzing data for 120,240,and 300 days,we generated fore-cast data for the next 40,80,and 100 days,respectively,using both ESN and LSTM.In terms of accuracy,ESN had the unique advantage of capturing non-linear data.Mean absolute error(MAE)was used to present the accuracy results.The MAEs of the data forecast by ESN were 0.024,0.024,and 0.025,which were,respectively,0.065,0.007,and 0.009 less than those of LSTM.In terms of con-vergence,ESN has a reservoir state-space structure,which makes it perform faster than other models.Root-mean-square error(RMSE)was used to present the con-vergence time.In our experiment,the RMSEs of ESN were 0.22,0.27,and 0.26,which were,respectively,0.08,0.01,and 0.12 less than those of LSTM.In terms of network structure,ESN consists only of input,reservoir,and output spaces,making it a much simpler model than the others.The proposed ESN was found to be an effective model that,compared to others,converges faster,forecasts more accurately,and builds time-series analyses more easily.

关键词： Stock data forecast echo state network deep learning

Analysis of prediction performance in wavelet minimum complexity echo state network

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The Journal of China Universities of Posts and Telecommunications 2013年第4期20卷 59-66页

作者： CUI Hong-yan FENG Chen LIU Yun-jie Key Laboratory of network System Architecture and Convergence Beijing University of Posts and Telecommunications School of Information and Communication Engineering Beijing University of Posts and Telecommunications

echo state network （ESN） has become one of the most popular recurrent neural networks （RNN） for its good prediction performance of non-linear time series and simple training process. But several problems still prevent ESN from becoming a widely used tool. The most prominent problem is its high complexity with lots of random parameters. Aiming at this problem, a minimum complexity ESN model （MCESN） was proposed. In this paper, we proposed a new wavelet minimum complexity ESN model （WMCESN） to improve the prediction accuracy and increase the practical applicability. Our new model inherits the characters of minimum complexity ESN model using the fixed parameters and simple circle topology. We injected wavelet neurons to replace the original neurons in internal reservoir and designed a wavelet parameter matrix to reduce the computing time. By using different datasets, our new model performed better than the minimum complexity ESN model with normal neurons, but only utilized tiny time cost. We also used our own packets of transmission control protocol （TCP） and user datagram protocol （UDP） dataset to prove that our model can deal with the data packet bit prediction problem well.

关键词： wavelet minimum complexity echo state network echo state network wavelet parameter matrix practical applicability

Constrained docking control for shipborne UAV SideArm recovery

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Chinese Journal of Aeronautics 2024年第5期37卷 39-59页

作者： Zikang SU Zhuolin XING Xuebing LI Chuntao LI Xinwei WANG Honglun WANG College of Automation Engineering Nanjing University of Aeronautics and AstronauticsNanjing 211106China School of Engineering and Materials Science Queen Mary University of LondonLondon E14NSUK School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China

In this work,we sought to investigate constrained docking control during shipborne SideArm recovery of an Unmanned Aerial Vehicle(UAV)under preassigned safe docking constraints,rough ocean environments,and different initial positions.The aim was to solve the UAV tracking-lag problem that manifests when attempting to dock with a rapidly moving SideArm and to improve the accuracy and rapidity of docking.First,together with the formulations of the shipborne SideArm system and environmental airflows,the affine nonlinear dynamics of the hook was established to reduce tracking lag.Then,echo state network approximators with good approximation capacity and low computational consumption were designed to accurately approximate the UAV’s unknown nonlinear dynamics.With feedforward compensation provided by these approximators,a nonlinear-mapping-based constrained docking control law was developed for shipborne SideArm recovery of UAVs.This approach to controlling the docking trajectory and the forward docking speed of the UAV can achieve rapid and exact docking with a moving SideArm,without violating the preassigned safe docking-constraint envelopes.Simulations under different docking scenarios were used to validate the effectiveness and advantages of the proposed docking-control algorithm.

关键词： SideArm recovery Unmanned aerial vehicle Docking control Nonlinear mapping echo state network

Lightweight Intrusion Detection Using Reservoir Computing

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Computers, Materials & Continua 2024年第1期78卷 1345-1361页

作者： Jiarui Deng Wuqiang Shen Yihua Feng Guosheng Lu Guiquan Shen Lei Cui Shanxiang Lyu Joint Laboratory on Cyberspace Security China Southern Power GridGuangzhou510080China College of Cyber Security Jinan UniversityGuangzhou510632China Guangdong Power Grid Company Limited Guangzhou510663China School of Computer Science and Technology Guangdong University of TechnologyGuangzhou510006China China Southern Power Grid Extra High Voltage Transmission CompanyGuangzhouChina

The blockchain-empowered Internet of Vehicles(IoV)enables various services and achieves data security and privacy,significantly advancing modern vehicle systems.However,the increased frequency of data transmission and complex network connections among nodes also make them more susceptible to adversarial attacks.As a result,an efficient intrusion detection system(IDS)becomes crucial for securing the IoV environment.Existing IDSs based on convolutional neural networks(CNN)often suffer from high training time and storage requirements.In this paper,we propose a lightweight IDS solution to protect IoV against both intra-vehicle and external threats.Our approach achieves superior performance,as demonstrated by key metrics such as accuracy and precision.Specifically,our method achieves accuracy rates ranging from 99.08% to 100% on the Car-Hacking dataset,with a remarkably short training time.

关键词： echo state network intrusion detection system Internet of Vehicles reservoir computing

A Prediction Method Based on Improved echo state network for COVID-19 Nonlinear Time Series

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Journal of Computer and Communications 2020年第12期8卷 113-122页

作者： Banteng Liu Wei Chen Yourong Chen Ping Sun Heli Jin Hao Chen College of Information Science and Technology Zhejiang Shuren University Hangzhou China School of Computer Science and Artificial Intelligence Changzhou University Changzhou China

This paper proposes a prediction method based on improved echo state network for COVID-19 nonlinear time series, which improves the echo state network from the reservoir topology and the output weight matrix, and adopt the ABC (Artificial Bee Colony) algorithm based on crossover and crowding strategy to optimize the parameters. Finally, the proposed method is simulated and the results show that it has stronger prediction ability for COVID-19 nonlinear time series.

关键词： COVID-19 Nonlinear Time Series Prediction echo state network

echo state network based on Phase Space Reconstruction:El Ni?o 3 Index Forecasting

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Echo State Network based on Phase Space Reconstruction:El Ni...

第32届中国控制与决策会议

作者： Shaoshuai Wang Jiezhou Yu Jun Meng College of Electrical Engineering Zhejiang University

Climate forecasting,especially for the abnormal weather like El Ni?o and La Ni?a etc.,is one of the most difficult tasks of time series prediction domain,because of its inherent difficulty of long-term forecasting.In this paper,we investigate whether there is a simple pattern to describe El Ni?o phenomenon that can be reconstructed by mathematical models and a multiple steps ahead predicting method is proposed,which is an improved echo state network（ESN） using the phase-space reconstruction（PSR）.PSR is to embed a one-dimensional time series sequence in a high-dimension phase space as the input of the ESN.Specifically,the method PSR-ESN can capture seasonal characteristics of the climate data and reduce the lag of prediction as much as possible.The method is evaluated on a real Ni?o 3 SST index data and it outperforms the other state-of-the-art baselines from the perspective of the magnitude and the lag.

关键词： Time Series Prediction El Ni?o SST index echo state network

echo state network-based visibility graph method for nonlinear time series prediction

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Echo state network-based visibility graph method for nonline...

第30届中国控制与决策会议

作者： Qian Li Yuguang Chen Nian Ao Xu Han Zhou Wu Key Laboratory of Dependable Service Computing in Cyber Physical Society (Chongqing University) of Ministry of Education College of Automation Chongqing University

For an echo state network（ESN）, the reservoir topology plays a very important role in network performance.However, the computing capability of classical random structure is limited, and it can not meet the prediction accuracy requirement of some highly complicated prediction tasks. Therefore, different algorithms to construct new reservoir topology based on complex network theory have been proposed, amongst of which, the visibility graph algorithm shows to be more effective and simple. In this paper, a new ESN based on the visibility graph（VGESN） is proposed to perform the prediction of Mackey-Glass chaotic system（MGS） and nonlinear autoregressive moving average（NARMA） time series. Furthermore, the network features of associated graphs are analysed, including the small-world feature and scalefree property. The simulation results indicate that the proposed VGESN has better prediction performance compared with traditional ESN. The analytical results of network characteristics also reveal that the MGS associated graph has small-world feature, while the NARMA associated graph has both small-world and scale-free property.

关键词： echo state network visibility graph network characteristics prediction

Remaining Useful Life Prediction Based on Stacked Sparse Autoencoder and echo state network

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Remaining Useful Life Prediction Based on Stacked Sparse Aut...

第三十九届中国控制会议

作者： Yinghua Yang Dandan Yao Xiaozhi Liu College of Information Science and Engineering Northeastern University

Nowadays, the prediction of industry components’ remaining useful life（RUL） has already become a hot topic. In this paper, a RUL prediction method based on stacked sparse autoencoder（SAE） and echo state network（ESN） is proposed.Autoencoder（AE） is an unsupervised learning method that can be used for feature extraction to obtain a health index（HI）. To enhance the performance of the HI, a sparse constraint is added and a stacked structure is used to increase depth. The RUL of the industry components is then predicted using a method that maps directly to the RUL value based on the health factor curve. With the capability of encapsulating dynamic time behavior and saving historical information of input data, ESN is selected as the prediction network. The proposed method is verified using the C-MPASS dataset. The experimental results show that the method has better performance than the stacked AE based method.

关键词： Remaining useful life stacked sparse autoencoder echo state network

A Prediction Method Based on Improved echo state network for COVID-19 Nonlinear Time Series

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A Prediction Method Based on Improved Echo State Network for...

作者： Banteng Liu Wei Chen Yourong Chen Ping Sun Heli Jin Hao Chen College of Information Science and Technology Zhejiang Shuren University School of Computer Science and Artificial Intelligence Changzhou University

This paper proposes a prediction method based on improved echo state network for COVID-19 nonlinear time series, which improves the echo state network from the reservoir topology and the output weight matrix, and adopt the ABC（Artificial Bee Colony） algorithm based on crossover and crowding strategy to optimize the parameters. Finally, the proposed method is simulated and the results show that it has stronger prediction ability for COVID-19 nonlinear time series.

关键词： COVID-19 Nonlinear Time Series Prediction echo state network