This study proposes two metrics using the nearest neighbors method to improve the accuracy of time-series forecasting. These two metrics can be treated as a hybrid forecasting approach to combine linear and non-linear...This study proposes two metrics using the nearest neighbors method to improve the accuracy of time-series forecasting. These two metrics can be treated as a hybrid forecasting approach to combine linear and non-linear forecasting techniques. One metric redefines the distance in k-nearest neighbors based on the coefficients of autoregression (AR) in time series. Meanwhile, an improvement to Kulesh's adaptive metrics in the nearest neighbors is also presented. To evaluate the performance of the two proposed metrics, three types of time-series data, namely deterministic synthetic data, chaotic time-series data and real time-series data, are predicted. Experimental results show the superiority of the proposed AR-enhanced k-nearest neighbors methods to the traditional k-nearest neighbors metric and Kulesh's adaptive metrics.展开更多
-In this paper, monthly mean SST data in a large area are used. After the spacial average of the data is carried out and the secular monthly means are substracted, a time series (Jan. 1951-Dec. 1985) of SST anomalies ...-In this paper, monthly mean SST data in a large area are used. After the spacial average of the data is carried out and the secular monthly means are substracted, a time series (Jan. 1951-Dec. 1985) of SST anomalies of the cold tongue water area in the eastern tropical Pacific Ocean is obtained. On the basis of the time series, an autoregression model, a self-exciting threshold autoregression model and an open loop autoregression model are developed respectively. The interannual variations are simulated by means of those models. The simulation results show that all the three models have made very good hindcasting for the nine El Nino events since 1951. In order to test the reliability of the open loop threshold model, extrapolated forecast was made for the period of Jan. 1986-Feb. 1987. It can be seen from the forecasting that the model could forecast well the beginning and strengthening stages of the recent El Nino event (1986-1987). Correlation coefficients of the estimations to observations are respectively 0. 84, 0. 88 and 0. 89. It is obvious that all the models work well and the open loop threshold one is the best. So the open loop threshold autoregression model is a useful tool for monitoring the SSTinterannual variation of the cold tongue water area in the Eastern Equatorial Pacific Ocean and for estimating the El Nino strength.展开更多
Network autoregression and factor model are effective methods for modeling network time series data.In this study,we propose a network autoregression model with a factor structure that incorporates a latent group stru...Network autoregression and factor model are effective methods for modeling network time series data.In this study,we propose a network autoregression model with a factor structure that incorporates a latent group structure to address nodal heterogeneity within the network.An iterative algorithm is employed to minimize a least-squares objective function,allowing for simultaneous estimation of both the parameters and the group structure.To determine the unknown number of groups and factors,a PIC criterion is introduced.Additionally,statistical inference of the estimated parameters is presented.To assess the validity of the proposed estimation and inference procedures,we conduct extensive numerical studies.We also demonstrate the utility of our model using a stock dataset obtained from the Chinese A-Share stock market.展开更多
本文以黄河利津站和浙江省白溪水库的月径流水文序列为例,在自相关分析的基础上,建立自回归autoregression模型,并参照其结构建立了相应的resilient back propagation神经网络预报模型.比较结果显示:(1)resilient back propagation模型...本文以黄河利津站和浙江省白溪水库的月径流水文序列为例,在自相关分析的基础上,建立自回归autoregression模型,并参照其结构建立了相应的resilient back propagation神经网络预报模型.比较结果显示:(1)resilient back propagation模型的模拟预报结果与序列的自相关性有密切关系;(2)当序列有较好的自相关性时,可参照autoregression模型建立相应的resilient back propagation模型;(3)与传统autoregression模型相比,resilient back propagation模型能取得更高的预报精度;且随着预报步长增加,resilient back propagation模型的优势更加明显.展开更多
Forecasting solar irradiance is a critical task in the renewable energy sector, as it provides essential information regarding the potential energy production from solar panels. This study aims to utilize the Vector A...Forecasting solar irradiance is a critical task in the renewable energy sector, as it provides essential information regarding the potential energy production from solar panels. This study aims to utilize the Vector Autoregression (VAR) model to forecast solar irradiance levels and weather characteristics in the San Francisco Bay Area. The results demonstrate a correlation between predicted and actual solar irradiance, indicating the effectiveness of the VAR model for this task. However, the model may not be sufficient for this region due to the requirement of additional weather features to reduce disparities between predictions and actual observations. Additionally, the current lag order in the model is relatively low, limiting its ability to capture all relevant information from past observations. As a result, the model’s forecasting capability is limited to short-term horizons, with a maximum horizon of four hours.展开更多
目的:鼻咽癌发病位置隐匿导致早期诊断率低,且具有明显的地域聚集性,是中国一个重要的公共卫生问题。本研究旨在通过2021年全球疾病负担(the Global Burden of Diseases,GBD)数据库分析中国鼻咽癌的疾病负担,为鼻咽癌的精准防控提供流...目的:鼻咽癌发病位置隐匿导致早期诊断率低,且具有明显的地域聚集性,是中国一个重要的公共卫生问题。本研究旨在通过2021年全球疾病负担(the Global Burden of Diseases,GBD)数据库分析中国鼻咽癌的疾病负担,为鼻咽癌的精准防控提供流行病学依据。方法:选取年龄标化发病率、病死率、伤残调整寿命年(disability adjusted life year,DALY)率作为疾病负担的评价指标,按照不同年龄、性别、社会人口学指数及其相关危险因素进行分层分析,同时应用差分自回归移动平均(autoregressive integrated moving average,ARIMA)模型和贝叶斯年龄-时期-队列分析模型(Bayesian age-period-cohort,BAPC)将年龄标化发病率预测至2050年。结果:2021年中国鼻咽癌年龄标化发病率、病死率、DALY率分别为3.4/10万、1.5/10万、48.7/10万,均高于同期全球水平。在所有年龄段,中国男性年龄标化发病率、病死率、DALY率均高于女性。中国鼻咽癌的疾病负担从1990至2021年随着社会人口学指数(socio-demographic index,SDI)的增高逐渐降低。中国归因于饮酒、吸烟、职业甲醛暴露的鼻咽癌疾病负担占比均高于全球水平,且在男性中尤为显著。模型预测中国及全球男性、女性、全人群的年龄标化发病率均提示从2022至2050年呈上升趋势。结论:既往30年中国鼻咽癌的疾病负担随着SDI的升高逐渐降低,但仍高于同期全球水平。同时,中国鼻咽癌的年龄标化发病率在未来30年呈上升趋势。中国仍需进一步增加医疗资源的投入以应对鼻咽癌的防控与诊疗,尤其针对高风险男性群体。展开更多
针对现有深度学习算法在壁画修复时,存在全局语义一致性约束不足及局部特征提取不充分,导致修复后的壁画易出现边界效应和细节模糊等问题,提出一种双向自回归Transformer与快速傅里叶卷积增强的壁画修复方法.首先,设计基于Transformer...针对现有深度学习算法在壁画修复时,存在全局语义一致性约束不足及局部特征提取不充分,导致修复后的壁画易出现边界效应和细节模糊等问题,提出一种双向自回归Transformer与快速傅里叶卷积增强的壁画修复方法.首先,设计基于Transformer结构的全局语义特征修复模块,利用双向自回归机制与掩码语言模型(masked language modeling,MLM),提出改进的多头注意力全局语义壁画修复模块,提高对全局语义特征的修复能力.然后,构建了由门控卷积和残差模块组成的全局语义增强模块,增强全局语义特征一致性约束.最后,设计局部细节修复模块,采用大核注意力机制(large kernel attention,LKA)与快速傅里叶卷积提高细节特征的捕获能力,同时减少局部细节信息的丢失,提升修复壁画局部和整体特征的一致性.通过对敦煌壁画数字化修复实验,结果表明,所提算法修复性能更优,客观评价指标均优于比较算法.展开更多
基金the National Natural Science Foundation of China(No.61203337)the Natural Science Foundation of Shanghai(No.12ZR1440200)
文摘This study proposes two metrics using the nearest neighbors method to improve the accuracy of time-series forecasting. These two metrics can be treated as a hybrid forecasting approach to combine linear and non-linear forecasting techniques. One metric redefines the distance in k-nearest neighbors based on the coefficients of autoregression (AR) in time series. Meanwhile, an improvement to Kulesh's adaptive metrics in the nearest neighbors is also presented. To evaluate the performance of the two proposed metrics, three types of time-series data, namely deterministic synthetic data, chaotic time-series data and real time-series data, are predicted. Experimental results show the superiority of the proposed AR-enhanced k-nearest neighbors methods to the traditional k-nearest neighbors metric and Kulesh's adaptive metrics.
文摘-In this paper, monthly mean SST data in a large area are used. After the spacial average of the data is carried out and the secular monthly means are substracted, a time series (Jan. 1951-Dec. 1985) of SST anomalies of the cold tongue water area in the eastern tropical Pacific Ocean is obtained. On the basis of the time series, an autoregression model, a self-exciting threshold autoregression model and an open loop autoregression model are developed respectively. The interannual variations are simulated by means of those models. The simulation results show that all the three models have made very good hindcasting for the nine El Nino events since 1951. In order to test the reliability of the open loop threshold model, extrapolated forecast was made for the period of Jan. 1986-Feb. 1987. It can be seen from the forecasting that the model could forecast well the beginning and strengthening stages of the recent El Nino event (1986-1987). Correlation coefficients of the estimations to observations are respectively 0. 84, 0. 88 and 0. 89. It is obvious that all the models work well and the open loop threshold one is the best. So the open loop threshold autoregression model is a useful tool for monitoring the SSTinterannual variation of the cold tongue water area in the Eastern Equatorial Pacific Ocean and for estimating the El Nino strength.
基金Supported by National Natural Science Foundation of China(72222009,71991472)。
文摘Network autoregression and factor model are effective methods for modeling network time series data.In this study,we propose a network autoregression model with a factor structure that incorporates a latent group structure to address nodal heterogeneity within the network.An iterative algorithm is employed to minimize a least-squares objective function,allowing for simultaneous estimation of both the parameters and the group structure.To determine the unknown number of groups and factors,a PIC criterion is introduced.Additionally,statistical inference of the estimated parameters is presented.To assess the validity of the proposed estimation and inference procedures,we conduct extensive numerical studies.We also demonstrate the utility of our model using a stock dataset obtained from the Chinese A-Share stock market.
文摘本文以黄河利津站和浙江省白溪水库的月径流水文序列为例,在自相关分析的基础上,建立自回归autoregression模型,并参照其结构建立了相应的resilient back propagation神经网络预报模型.比较结果显示:(1)resilient back propagation模型的模拟预报结果与序列的自相关性有密切关系;(2)当序列有较好的自相关性时,可参照autoregression模型建立相应的resilient back propagation模型;(3)与传统autoregression模型相比,resilient back propagation模型能取得更高的预报精度;且随着预报步长增加,resilient back propagation模型的优势更加明显.
文摘Forecasting solar irradiance is a critical task in the renewable energy sector, as it provides essential information regarding the potential energy production from solar panels. This study aims to utilize the Vector Autoregression (VAR) model to forecast solar irradiance levels and weather characteristics in the San Francisco Bay Area. The results demonstrate a correlation between predicted and actual solar irradiance, indicating the effectiveness of the VAR model for this task. However, the model may not be sufficient for this region due to the requirement of additional weather features to reduce disparities between predictions and actual observations. Additionally, the current lag order in the model is relatively low, limiting its ability to capture all relevant information from past observations. As a result, the model’s forecasting capability is limited to short-term horizons, with a maximum horizon of four hours.
文摘目的:鼻咽癌发病位置隐匿导致早期诊断率低,且具有明显的地域聚集性,是中国一个重要的公共卫生问题。本研究旨在通过2021年全球疾病负担(the Global Burden of Diseases,GBD)数据库分析中国鼻咽癌的疾病负担,为鼻咽癌的精准防控提供流行病学依据。方法:选取年龄标化发病率、病死率、伤残调整寿命年(disability adjusted life year,DALY)率作为疾病负担的评价指标,按照不同年龄、性别、社会人口学指数及其相关危险因素进行分层分析,同时应用差分自回归移动平均(autoregressive integrated moving average,ARIMA)模型和贝叶斯年龄-时期-队列分析模型(Bayesian age-period-cohort,BAPC)将年龄标化发病率预测至2050年。结果:2021年中国鼻咽癌年龄标化发病率、病死率、DALY率分别为3.4/10万、1.5/10万、48.7/10万,均高于同期全球水平。在所有年龄段,中国男性年龄标化发病率、病死率、DALY率均高于女性。中国鼻咽癌的疾病负担从1990至2021年随着社会人口学指数(socio-demographic index,SDI)的增高逐渐降低。中国归因于饮酒、吸烟、职业甲醛暴露的鼻咽癌疾病负担占比均高于全球水平,且在男性中尤为显著。模型预测中国及全球男性、女性、全人群的年龄标化发病率均提示从2022至2050年呈上升趋势。结论:既往30年中国鼻咽癌的疾病负担随着SDI的升高逐渐降低,但仍高于同期全球水平。同时,中国鼻咽癌的年龄标化发病率在未来30年呈上升趋势。中国仍需进一步增加医疗资源的投入以应对鼻咽癌的防控与诊疗,尤其针对高风险男性群体。
文摘针对现有深度学习算法在壁画修复时,存在全局语义一致性约束不足及局部特征提取不充分,导致修复后的壁画易出现边界效应和细节模糊等问题,提出一种双向自回归Transformer与快速傅里叶卷积增强的壁画修复方法.首先,设计基于Transformer结构的全局语义特征修复模块,利用双向自回归机制与掩码语言模型(masked language modeling,MLM),提出改进的多头注意力全局语义壁画修复模块,提高对全局语义特征的修复能力.然后,构建了由门控卷积和残差模块组成的全局语义增强模块,增强全局语义特征一致性约束.最后,设计局部细节修复模块,采用大核注意力机制(large kernel attention,LKA)与快速傅里叶卷积提高细节特征的捕获能力,同时减少局部细节信息的丢失,提升修复壁画局部和整体特征的一致性.通过对敦煌壁画数字化修复实验,结果表明,所提算法修复性能更优,客观评价指标均优于比较算法.
