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TSCND:Temporal Subsequence-Based Convolutional Network with Difference for Time Series Forecasting

Computers, Materials & Continua 2024年第3期78卷 3665-3681页

作者： Haoran Huang Weiting Chen Zheming Fan MOE Research Center of Software/Hardware Co-Design Engineering East China Normal UniversityShanghai200062China

Time series forecasting plays an important role in various fields, such as energy, finance, transport, and weather. Temporal convolutional networks (TCNs) based on dilated causal convolution have been widely used in time series forecasting. However, two problems weaken the performance of TCNs. One is that in dilated casual convolution, causal convolution leads to the receptive fields of outputs being concentrated in the earlier part of the input sequence, whereas the recent input information will be severely lost. The other is that the distribution shift problem in time series has not been adequately solved. To address the first problem, we propose a subsequence-based dilated convolution method (SDC). By using multiple convolutional filters to convolve elements of neighboring subsequences, the method extracts temporal features from a growing receptive field via a growing subsequence rather than a single element. Ultimately, the receptive field of each output element can cover the whole input sequence. To address the second problem, we propose a difference and compensation method (DCM). The method reduces the discrepancies between and within the input sequences by difference operations and then compensates the outputs for the information lost due to difference operations. Based on SDC and DCM, we further construct a temporal subsequence-based convolutional network with difference (TSCND) for time series forecasting. The experimental results show that TSCND can reduce prediction mean squared error by 7.3% and save runtime, compared with state-of-the-art models and vanilla TCN.

关键词： Difference data prediction time series temporal convolutional network dilated convolution

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PRODUCT data prediction WITH UNCERTAINTY IN PRODUCT LIFE CYCLE DESIGN

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Chinese Journal of Mechanical Engineering 2003年第3期16卷 296-299页

作者： YuSuiran WangChengtao KimuraFumihiko ShanghaiJiaotongUniversity Shanghai200030China DepartmentofPredsionMachineryEng. UniversityofTokyoTokyo113.8656Japan

Various kinds of data are used in new product design and more accurate datamake the design results more reliable. Even though part of product data can be available directlyfrom the existing similar products, there still leaves a great deal of data unavailable. This makesdata prediction a valuable work. A method that can predict data of product under development basedon the existing similar products is proposed. Fuzzy theory is used to deal with the uncertainties indata prediction process. The proposed method can be used in life cycle design, life cycleassessment (LCA) etc. Case study on current refrigerator is used as a demonstration example.

关键词： data prediction Uncertainty Life cycle design

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data prediction and Real-time State Estimation of Distribution Network

Data Prediction and Real-time State Estimation of Distributi...

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作者： Wen-si HUANG Xin LU Hui LI Zhao-yan LIU Xiao-wei QI Liang AI Ren-jun WANG Hong-jun GAO State Grid Xintong Yili Technology Co.Ltd State Grid Beijing Electric Power Company State Grid Beijing Electric Power Company Haidian Power Supply Company College of Electric Engineering Sichuan University

With the improvement of real-time measurement technology in distribution network,the data collected by power dispatching department is explosively growing.As a result,the real-time detection and analysis of the operation status of distribution network is facing new challenges.In this paper,a prediction model based on the combination of fuzzy c-means（FCM） algorithm and BP neural network is proposed to describe the range of future distribution network operation data.The purpose of this work is to provide a reference range for real-time operation state analysis.Then,considering the problem of massive measurement data in distribution network,a robust state estimation method based on exponential function weighted least squares（EFWLS） is proposed,which has better accuracy and efficiency than traditional weighted least squares（WLS） method.Finally,the accuracy of the prediction model is verified by comparing the predicted data with the real data,and the advantages of the proposed state estimation method are verified by comparing the results of different methods.

关键词： data prediction Fuzzy C-means BP neural network State estimation method

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Time Series Forecasting Fusion Network Model Based on Prophet and Improved LSTM

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Computers, Materials & Continua 2023年第2期74卷 3199-3219页

作者： Weifeng Liu Xin Yu Qinyang Zhao Guang Cheng Xiaobing Hou Shengqi He College of Urban Rall Translt and Logistics Beijing Union UniversityBeijing100101China Beijing Key Laboratory of Information Service Engineering Beijing Union UniversityBeijing100101China Faculty of Business Economicsand LawThe University of QueenslandBrisbane4072Australia Department of Oncology Wangjing HospitalChina Academy of Chinese Medical SciencesBeijing100102China

Time series forecasting and analysis are widely used in many fields and application scenarios.Time series historical data reflects the change pattern and trend,which can serve the application and decision in each application scenario to a certain extent.In this paper,we select the time series prediction problem in the atmospheric environment scenario to start the application research.In terms of data support,we obtain the data of nearly 3500 vehicles in some cities in China fromRunwoda Research Institute,focusing on the major pollutant emission data of non-road mobile machinery and high emission vehicles in Beijing and Bozhou,Anhui Province to build the dataset and conduct the time series prediction analysis experiments on them.This paper proposes a P-gLSTNet model,and uses Autoregressive Integrated Moving Average model(ARIMA),long and short-term memory(LSTM),and Prophet to predict and compare the emissions in the future period.The experiments are validated on four public data sets and one self-collected data set,and the mean absolute error(MAE),root mean square error(RMSE),and mean absolute percentage error(MAPE)are selected as the evaluationmetrics.The experimental results show that the proposed P-gLSTNet fusion model predicts less error,outperforms the backbone method,and is more suitable for the prediction of time-series data in this scenario.

关键词： Time series data prediction regression analysis long short-term memory network prophet

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Optimizing slope safety factor prediction via stacking using sparrow search algorithm for multi-layer machine learning regression models

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Journal of Mountain Science 2023年第10期20卷 2852-2868页

作者： SHUI Kuan HOU Ke-peng HOU Wen-wen SUN Jun-long SUN Hua-fen Faculty of Land Resource Engineering Kunming University of Science and TechnologyKunming 650093China Yunnan Key Laboratory of Sino-German Blue Mining and Utilization of Special Underground Space Kunming 650093China College of Water Resource and Hydropower Sichuan UniversityChengdu 610065China

The safety factor is a crucial quantitative index for evaluating slope stability.However,the traditional calculation methods suffer from unreasonable assumptions,complex soil composition,and inadequate consideration of the influencing factors,leading to large errors in their calculations.Therefore,a stacking ensemble learning model(stacking-SSAOP)based on multi-layer regression algorithm fusion and optimized by the sparrow search algorithm is proposed for predicting the slope safety factor.In this method,the density,cohesion,friction angle,slope angle,slope height,and pore pressure ratio are selected as characteristic parameters from the 210 sets of established slope sample data.Random Forest,Extra Trees,AdaBoost,Bagging,and Support Vector regression are used as the base model(inner loop)to construct the first-level regression algorithm layer,and XGBoost is used as the meta-model(outer loop)to construct the second-level regression algorithm layer and complete the construction of the stacked learning model for improving the model prediction accuracy.The sparrow search algorithm is used to optimize the hyperparameters of the above six regression models and correct the over-and underfitting problems of the single regression model to further improve the prediction accuracy.The mean square error(MSE)of the predicted and true values and the fitting of the data are compared and analyzed.The MSE of the stacking-SSAOP model was found to be smaller than that of the single regression model(MSE=0.03917).Therefore,the former has a higher prediction accuracy and better data fitting.This study innovatively applies the sparrow search algorithm to predict the slope safety factor,showcasing its advantages over traditional methods.Additionally,our proposed stacking-SSAOP model integrates multiple regression algorithms to enhance prediction accuracy.This model not only refines the prediction accuracy of the slope safety factor but also offers a fresh approach to handling the intricate soil composition and other influencing factors,m

关键词： Multi-layer regression algorithm fusion Stacking gensemblelearning Sparrow search algorithm Slope safety factor data prediction

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Improved data Analysis Algorithm based on Multi-feature Network Construction

Improved Data Analysis Algorithm based on Multi-feature Netw...

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作者： Wenhua Guo School of Beijing Industry University

Network information has become an important factor in today’s social environment and network environment.With the large coverage of network data traffic and the new generation of network technology,illegal network data is also constantly invading the network environment,which has caused a serious network security threat.Therefore,the analysis and research of network data and the pre-judgment of feature types are of great significance.Based on the existing theoretical techniques,this paper proposes a data Processing Algorithm Based on The Website Coding Structure（DPA） and a Multi-feature Network Construction Improvement Algorithm（MCIA）,processing network data and analysis type prediction features.Experiments show that the data processing algorithm based on the website coding structure has the advantage of targeted processing of website data.It also verifies the validity of the proposed Digital Neighborhood Feature Set Algorithm（DNFA） and the improved algorithm for multi-feature network construction for data analysis.Application,compared with the existing single feature set,the pre-judgment basis for constructing multiple feature sets is more reliable,and the pre-judgment basis is more credible.

关键词： Network data Feature set analysis data prediction

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