Graph Construction Method for GNN-Based Multivariate Time-Series Forecasting

Multivariate time-series forecasting(MTSF)plays an important role in diverse real-world applications.To achieve better accuracy in MTSF,time-series patterns in each variable and interrelationship patterns between variables should be considered together.Recently,graph neural networks(GNNs)has gained much attention as they can learn both patterns using a graph.For accurate forecasting through GNN,a well-defined graph is required.However,existing GNNs have limitations in reflecting the spectral similarity and time delay between nodes,and consider all nodes with the same weight when constructing graph.In this paper,we propose a novel graph construction method that solves aforementioned limitations.We first calculate the Fourier transform-based spectral similarity and then update this similarity to reflect the time delay.Then,we weight each node according to the number of edge connections to get the final graph and utilize it to train the GNN model.Through experiments on various datasets,we demonstrated that the proposed method enhanced the performance of GNN-based MTSF models,and the proposed forecasting model achieve of up to 18.1%predictive performance improvement over the state-of-the-art model.

A Hybrid Neural Network Model for Marine Dissolved Oxygen Concentrations Time-Series Forecasting Based on Multi-Factor Analysis and a Multi-Model Ensemble

Dissolved oxygen(DO)is an important indicator of aquaculture,and its accurate forecasting can effectively improve the quality of aquatic products.In this paper,a new DO hybrid forecasting model is proposed that includes three stages:multi-factor analysis,adaptive decomposition,and an optimizationbased ensemble.First,considering the complex factors affecting DO,the grey relational(GR)degree method is used to screen out the environmental factors most closely related to DO.The consideration of multiple factors makes model fusion more effective.Second,the series of DO,water temperature,salinity,and oxygen saturation are decomposed adaptively into sub-series by means of the empirical wavelet transform(EWT)method.Then,five benchmark models are utilized to forecast the sub-series of EWT decomposition.The ensemble weights of these five sub-forecasting models are calculated by particle swarm optimization and gravitational search algorithm(PSOGSA).Finally,a multi-factor ensemble model for DO is obtained by weighted allocation.The performance of the proposed model is verified by timeseries data collected by the pacific islands ocean observing system(PacIOOS)from the WQB04 station at Hilo.The evaluation indicators involved in the experiment include the Nash–Sutcliffe efficiency(NSE),Kling–Gupta efficiency(KGE),mean absolute percent error(MAPE),standard deviation of error(SDE),and coefficient of determination(R^(2)).Example analysis demonstrates that:①The proposed model can obtain excellent DO forecasting results;②the proposed model is superior to other comparison models;and③the forecasting model can be used to analyze the trend of DO and enable managers to make better management decisions.

Weighted Time-Variant Slide Fuzzy Time-Series Models for Short-Term Load Forecasting

Short-term load forecast plays an important role in the day-to-day operation and scheduling of generating units. Season and temperature are the most important factors that affect the load change, but random factors such as big sport events or popular TV shows can change demand consumption in particular hours, which will lead to sudden load changes. A weighted time-variant slide fuzzy time-series model (WTVS) for short-term load forecasting is proposed to improve forecasting accuracy. The WTVS model is divided into three parts, including the data preprocessing, the trend training and the load forecasting. In the data preprocessing phase, the impact of random factors will be weakened by smoothing the historical data. In the trend training and load forecasting phase, the seasonal factor and the weighted historical data are introduced into the Time-variant Slide Fuzzy Time-series Models (TVS) for short-term load forecasting. The WTVS model is tested on the load of the National Electric Power Company in Jordan. Results show that the proposed WTVS model achieves a significant improvement in load forecasting accuracy as compared to TVS models.

Diffusionmodels for time-series applications: a survey

Diffusion models, a family of generative models based on deep learning, have become increasinglyprominent in cutting-edge machine learning research. With distinguished performance in generating samples thatresemble the observed data, diffusion models are widely used in image, video, and text synthesis nowadays. Inrecent years, the concept of diffusion has been extended to time-series applications, and many powerful models havebeen developed. Considering the deficiency of a methodical summary and discourse on these models, we providethis survey as an elementary resource for new researchers in this area and to provide inspiration to motivate futureresearch. For better understanding, we include an introduction about the basics of diffusion models. Except forthis, we primarily focus on diffusion-based methods for time-series forecasting, imputation, and generation, andpresent them, separately, in three individual sections. We also compare different methods for the same applicationand highlight their connections if applicable. Finally, we conclude with the common limitation of diffusion-basedmethods and highlight potential future research directions.

Influence of vapor pressure deficit on vegetation growth in China

Vapor pressure deficit(VPD)plays a crucial role in determining plant physiological functions and exerts a substantial influence on vegetation,second only to carbon dioxide(CO_(2)).As a robust indicator of atmospheric water demand,VPD has implications for global water resources,and its significance extends to the structure and functioning of ecosystems.However,the influence of VPD on vegetation growth under climate change remains unclear in China.This study employed empirical equations to estimate the VPD in China from 2000 to 2020 based on meteorological reanalysis data of the Climatic Research Unit(CRU)Time-Series version 4.06(TS4.06)and European Centre for Medium-Range Weather Forecasts(ECMWF)Reanalysis 5(ERA-5).Vegetation growth status was characterized using three vegetation indices,namely gross primary productivity(GPP),leaf area index(LAI),and near-infrared reflectance of vegetation(NIRv).The spatiotemporal dynamics of VPD and vegetation indices were analyzed using the Theil-Sen median trend analysis and Mann-Kendall test.Furthermore,the influence of VPD on vegetation growth and its relative contribution were assessed using a multiple linear regression model.The results indicated an overall negative correlation between VPD and vegetation indices.Three VPD intervals for the correlations between VPD and vegetation indices were identified:a significant positive correlation at VPD below 4.820 hPa,a significant negative correlation at VPD within 4.820–9.000 hPa,and a notable weakening of negative correlation at VPD above 9.000 hPa.VPD exhibited a pronounced negative impact on vegetation growth,surpassing those of temperature,precipitation,and solar radiation in absolute magnitude.CO_(2) contributed most positively to vegetation growth,with VPD offsetting approximately 30.00%of the positive effect of CO_(2).As the rise of VPD decelerated,its relative contribution to vegetation growth diminished.Additionally,the intensification of spatial variations in temperature and precipitation accentuated the spatial heterogeneity in the impact of VPD on vegetation growth in China.This research provides a theoretical foundation for addressing climate change in China,especially regarding the challenges posed by increasing VPD.

含Flat Fuzzy参数的回归预测模型的探讨

在现实世界中,有这样一类不确定的现象,即事件发生与否是确定的,但其本身是不分明的。对此,如何进行回归分析,统计预测呢?这是一个具有广阔研究前景的领域。文[1]讨论了一类不分明自回归预测模型,本文则在此基础上讨论含flat fuzzy参数的线性回归预测模型。同时把这类模型推广到时间序列中去,建立了Fuzzy自回归模型。笔者运用了flat fuzzy数的有关性质,把确定flat fuzzy参数A_j,归结为求解含参变量的线性规划问题,从而避免了用经典最小二乘法时,参数A_j不可微的麻烦,成功地确定了A_j,建立了回归预测模型,这就为回归统计预测,提供了另一种方法。

Deep learning for time series forecasting:The electric load case

Management and efficient operations in critical infrastructures such as smart grids take huge advantage of accurate power load forecasting,which,due to its non-linear nature,remains a challenging task.Recently,deep learning has emerged in the machine learning field achieving impressive performance in a vast range of tasks,from image classification to machine translation.Applications of deep learning models to the electric load forecasting problem are gaining interest among researchers as well as the industry,but a comprehensive and sound comparison among different-also traditional-architectures is not yet available in the literature.This work aims at filling the gap by reviewing and experimentally evaluating four real world datasets on the most recent trends in electric load forecasting,by contrasting deep learning architectures on short-term forecast(oneday-ahead prediction).Specifically,the focus is on feedforward and recurrent neural networks,sequence-to-sequence models and temporal convolutional neural networks along with architectural variants,which are known in the signal processing community but are novel to the load forecasting one.

A Dynamic Forecasting System with Applications in Production Logistics

Production logistics involve the co-ordination of ac tivities such as production and materials control (PMC), inventory management, p roduct life cycle management, etc. Those activities demand for an accurate forec asting model. However, the conventional methods of making sell and buy decision based on human forecast or conventional moving average and exponential smoothing methods is no longer be sufficient to meet the future need. Furthermore, the un derlying statistics of the market information change from time to time due to a number of reasons such as change of global economic environment, government poli cies and business risks. This demands for highly adaptive forecasting model which is robust enough to response and adapt well to the fast changes in the dat a characteristics, in other words, the trajectory of the "dynamic characteristic s" of the data. In this paper, an adaptive time-series modelling method was proposed for short -term dynamic forecasting. The method employs an autoregressive (AR) time-seri es model to carry out the forecasting process. A modified least mean square (MLM S) adaptive filter algorithm was established for adjusting the AR model coeffici ents so as to minimise the sum of squared of forecasting errors. A prototype dyn amic forecasting system was built based on the adaptive time-series modelling m ethod. Basically, the dynamic forecasting system can be divided into two phases, i.e. the Learning Phase and the Application Phase. The learning procedures star t with the determination of upper limit of the adaptation gain based on the conv ergence in the mean square criterion. Hence, the optimum ELMS filter parameters are determined using an iteration algorithm which changes each filter parameter i.e. the order, the adaptation gain andthe values initial coefficient vector on e by one inside a predetermined iteration range. The set of parameters which giv es the minimum value for sum of squared errors within the iteration range is sel ected as the optimum set of filter parameters. In the Application Phase, the sys tem is operated under a real-time environment. The sampled data is processed by the optimised ELMS filter and the forecasted data are calculated based on the a daptive time-series model. The error of forecasting is continuously monitored w ithin the predefined tolerance. When the system detects excessive forecasting er ror, a feedback alarm signal was issued for system re-calibration. Experimental results indicated that the convergence rate and sum of squared erro rs during initial adaptation could be significantly improved using the MLMS algorithm. The performance of the system was verified through a series of experi ments conducted on the forecast of materials demand and costing in productio n logistics. Satisfactory results were achieved with the forecast errors confini ng within in most instances. Further applications of the system can be found i n sales demand forecast, inventory management as well as collaborative planning, forecast and replenishment (CPFR) in logistics engineering.

Forecasting International Tourism Regional Expenditure

The vast majority of tourism forecasting studies have centered on tourist arrivals at an aggregated level.Little research has been done of forecasting tourist expenditure at a national level let alone at a regional level.This study uses expenditure data to assess the relative economic impact of tourism into regional areas.By comparing five time-series models(the Na?ve,Holt,ARMA and Basic Structural Model(BSM)with and without intervention),and three econometric models(the Vector Autoregressive(VAR)model and the Time Varying Parameter(TVP)with and without intervention),the study sought to find the most accurate model for forecasting tourism expenditure two years ahead for each of the 31 provinces of China's Mainland.The results show that TVP models outperform other time series and econometric models.The research also provides practical management outcomes by providing methods for forecasting tourist expenditure as an indicator of economic growth in China’s provinces.The research concludes with the findings on the most appropriate model for regional forecasting and potential new variables suitable at the regional level.

Inter-hour direct normal irradiance forecast with multiple data types and time-series

Boosted by a strong solar power market,the electricity grid is exposed to risk under an increasing share of fluctuant solar power.To increase the stability of the electricity grid,an accurate solar power forecast is needed to evaluate such fluctuations.In terms of forecast,solar irradiance is the key factor of solar power generation,which is affected by atmospheric conditions,including surface meteorological variables and column integrated variables.These variables involve multiple numerical timeseries and images.However,few studies have focused on the processing method of multiple data types in an interhour direct normal irradiance(DNI)forecast.In this study,a framework for predicting the DNI for a 10-min time horizon was developed,which included the nondimensionalization of multiple data types and time-series,development of a forecast model,and transformation of the outputs.Several atmospheric variables were considered in the forecast framework,including the historical DNI,wind speed and direction,relative humidity time-series,and ground-based cloud images.Experiments were conducted to evaluate the performance of the forecast framework.The experimental results demonstrate that the proposed method performs well with a normalized mean bias error of 0.41%and a normalized root mean square error(n RMSE)of20.53%,and outperforms the persistent model with an improvement of 34%in the nRMSE.

基于对数二次指数平滑的港口吞吐量预测

港口吞吐量预测是港口总体布局规划的重要前期工作,这项工作影响深远。以某港口为例,简介了二次指数平滑预测港口吞吐量过程;提出了用对数二次指数平滑预测港口吞吐量;分析了对数二次指数平滑的优点;得出结论:对数二次指数平滑模型比较适合快速成长的港口进行吞吐量定量预测,可为这些港口规划决策提供科学依据,供有关部门参考。

近水平煤层开采地表移动规律研究

将重庆基岩裸露地区地下开采引起的地表移动作为研究对象,探讨了近水平煤层开采条件下地表移动规律.根据矿区的地质构造和近水平煤层赋存的特点,通过建立地表位移观测站,利用长期的实测结果,综合分析了不同的回采工作面地表下沉和水平移动的观测数据,并采用MATLAB软件进行数据处理,拟合得出地表下沉和水平移动曲线及相关参数.为进行工程类比和预测,建议采用剖面函数法对实测数据进行计算机处理,有关移动变形参数应标准化.

基于3次指数平滑的水稻生产综合机械化发展水平预测

水稻机械化生产的发展过程因受外部因素的干扰,呈现出非线性的特点。为此,利用3次指数平滑的时间序列法进行水稻生产综合机械化发展水平的预测,能排除外部的偶然干扰,平稳地反映出水稻生产综合机械化水平的发展趋势,取得较贴近实际的预测结果,为区域农业机械化的发展规划提供决策依据。

圆柱平底螺旋立铣刀动态切削力仿真与应用

采用瞬时刚性力模型进行平底螺旋立铣刀动态切削力建模,推导出铣削切削力系数辨识公式。在卧式加工中心HMC63上进行7075铝合金全齿铣削切削力测试试验,辨识出切削力系数,并利用MATLAB进行切削力数值仿真。对试验结果和仿真结果进行对比,结果表明切削力建模是有效的,能够较准确地预测铣削力,进行切削力的预测和监测,用于工件加工变形控制。同时切削力系数可以进一步进行切削稳定性分析,提高航空结构件的加工精度和效率。

平顶山市学龄人口数量与结构的演进预测分析

学龄人口数量与结构的波动对基础教育区域均衡发展研究具有直接而深远的影响。基于全国第六次人口普查(平顶山市)资料,运用时间序列平滑预测法和改进的灰色预测模型法对未来学龄人口进行预测,并从未来学龄人口数量演进的角度分析其对基础教育区域均衡发展的影响.

物流基础设施网络节点的动态选址研究

物流基础设施网络节点的选址决策是一个长期决策,而随着时间的推移,需求和成本模式会随之变化,那么原来的选址决策就可能不是最优的,这时就需要确定一个随时间变化的选址方案,这个过程就是物流基础设施网络节点的动态选址。本文采用时间序列平滑预测法对需求进行预测,并据此用静态选址模型(重心法)得出结论,再运用动态规划技术,找出一个物流基础设施网络节点动态最优选址—再选址方案,使得计划期内的累积总利润现值最大化,并引入预测准确性因子来提高预测的准确性。

开发连续平压机动向预测的研究

介绍了我国林业资源的现状和人造板工业产业政策的特点，对国内外人造板机械的发展趋势、节能与保护环境以及引进产品国产化的需要作了论述对有关背景材料分析研究结果表明：因为符合“可持续发展”这个国民经济的总原则。

最优加权几何平均组合预测在短期电价预测中的应用

准确的短期电价预测可为市场参与者的竞价策略提供指导,直接影响着参与者的利益。针对电价预测的精确度问题,引入了最优加权几何平均组合预测方法,它综合利用了二次指数平滑、自适应模糊神经网络和修正的灰色模型三种方法提供的有用信息,并且该组合预测模型的误差平方和小于各单一预测方法的误差平方和,因此进一步提高了预测结果的准确性。最后用算例验证了该组合预测方法的可行性。

轨道交通客流预测对车辆编组的影响

文章分析了客流的分布情况,并且通过积分预测法分别对平峰期、高峰期以及平峰转高峰期进行了客流量预测,主要以北京城市轨道1号线为例,对车辆的2种编组方式进行了说明,给出了满员和超员时的数据,通过客流预测可以发现有效的编组不仅可以为乘客出行带来方便,还能减少不必要的资源浪费。

INTRCO平车的可靠性应用

介绍了可靠性及可靠性技术在铁路产品应用中的意义。定性分析了可靠性应用在INTRCO平车设计中的基本方法,并对分析结果进行总结,找出不足,提出建议。