## Temporally Weighted(时间加权)研究综述

Temporally Weighted 时间加权 - In addition, we propose a temporally decaying sampling algorithm with unbiased estimators for studying networks that evolve in continuous time, where the strength of links is a function of time, and the motif patterns are temporally weighted.^{[1]}As such, this study develops a comprehensive model that combines the expanded stochastic impacts by regression on population, affluence, and technology (STIRPAT) and the geographically and temporally weighted (GWTR) models to explore the spatial effects of three technology progress channels (research and development investment, technology spillover related to FDI, and DS) on the CO2 emissions in China from six sectors during 2000–2017.

^{[2]}

此外，我们提出了一种具有无偏估计量的时间衰减采样算法，用于研究在连续时间中演化的网络，其中链接的强度是时间的函数，并且主题模式是时间加权的。

^{[1]}因此，本研究开发了一个综合模型，该模型结合了人口、富裕和技术回归的扩展随机影响 (STIRPAT) 和地理和时间加权 (GWTR) 模型，以探索三个技术进步渠道（研究和2000-2017 年中国六个行业的 CO2 排放的发展投资、与 FDI 相关的技术溢出和 DS）。

^{[2]}

## geographically weighted regression 地理加权回归

From a statistical point of view, this study respectively provided evidence for the existence of spatial non-stationarity and temporal non-stationarity in winter wheat yield prediction based on geographically weighted regression (GWR) and temporally weighted regression (TWR).^{[1]}The parameters of the functions that describe the behavior of the housing market are estimated through applying different types of statistical models, including ordinary least squares (OLS), geographically weighted regression (GWR) and geographically and temporally weighted regression (GTWR).

^{[2]}ABSTRACT Geographically weighted regression (GWR) and geographically and temporally weighted regression (GTWR) are classic methods for estimating non-stationary relationships.

^{[3]}To help bridge this gap, this study takes the sample commercial land prices in the main urban area of Hangzhou from 2006 to 2015 as the empirical research object and investigates the spatiotemporal evolution mechanism of urban commercial land prices through a comparative analysis of the multiple regression analysis (MRA) with ordinary least squares (OLS), the geographically weighted regression (GWR), the temporally weighted regression (TWR), and the geographically and temporally weighted regression (GTWR) models.

^{[4]}The Geographically and Temporally Weighted Regression (GTWR) model is a development of the Geographically Weighted Regression (GWR) model.

^{[5]}In this paper, the bootstrap test in the geographically weighted regression (GWR) literature is extended to geographically and temporally weighted regression (GTWR) models for identifying homogeneous explanatory variables and spatiotemporally heterogeneous ones.

^{[6]}

从统计学的角度，本研究分别为基于地理加权回归（GWR）和时间加权回归（TWR）的冬小麦产量预测存在空间非平稳性和时间非平稳性提供了证据。

^{[1]}描述房地产市场行为的函数参数是通过应用不同类型的统计模型来估计的，包括普通最小二乘法（OLS）、地理加权回归（GWR）和地理和时间加权回归（GTWR）。

^{[2]}nan

^{[3]}nan

^{[4]}nan

^{[5]}nan

^{[6]}

## coordination degree model 协调度模型

In terms of methodology, these evaluation models are subsequently combined with CCDM (Coupling coordination degree model) and GTWR (Geographically and Temporally Weighted Regression) models to measure and analyze coupling degree and spatio-temporal heterogeneity of UAEE.^{[1]}Therefore, in viewing of this, this study integrated coupling coordination degree model (CCDM) and geographically and temporally weighted regression (GTWR) to measure the interaction relationship and spatiotemporal heterogeneity between urbanization and ecosystem health (UAEH) in Chongqing at the county scale from 1997 to 2015.

^{[2]}To comprehensively understand the law of urban-rural relationship and propose scientific measures of urban-rural coordinated development in Northeast China, this study uses the coupling coordination degree model and geographically and temporally weighted regression (GTWR) model to analyze the spatial-temporal patterns and the corresponding driving mechanisms of its urban-rural coordination since 1990.

^{[3]}

在方法论上，这些评估模型随后与CCDM（耦合协调度模型）和GTWR（地理和时间加权回归）模型相结合，测量和分析UAEE的耦合度和时空异质性。

^{[1]}因此，有鉴于此，本研究将耦合协调度模型（CCDM）与地理时空加权回归（GTWR）相结合，测算1997年以来重庆市县域城市化与生态系统健康（UAEH）的交互关系和时空异质性。到 2015 年。

^{[2]}nan

^{[3]}

## two step method 两步法

Based on the spatiotemporally weighted two-step method (STW-TSM), the spatiotemporal characteristics of the residual microwave brightness temperature (MBT) with the Mw7.^{[1]}By employing the spatio-temporally weighted two-step method (STW-TSM) and microwave brightness temperature (MBT) data from AMSR-2 instrument on board Aqua satellite, this paper investigates carefully the spatiotemporal features of multi-frequency MBT anomalies relating to the earthquake.

^{[2]}

基于时空加权两步法（STW-TSM），剩余微波亮温（MBT）与Mw7的时空特征。

^{[1]}本文利用Aqua卫星上AMSR-2仪器的时空加权两步法（STW-TSM）和微波亮温（MBT）数据，仔细研究了多频MBT异常的时空特征。地震。

^{[2]}

## Geographically Temporally Weighted 地理时间加权

Using the Geographically Temporally Weighted Regression (GTWR), this study explores how RETI affects industrial pollution from a spatial heterogeneity perspective.^{[1]}Geographically Temporally Weighted Regression (GTWR) is used to analyze determinants of the Chinese ecological civilization performance.

^{[2]}We used geographic detectors and a geographically temporally weighted regression model (GTWR) to explore the rural settlements’ evolution and driving mechanism in Hubei Province from 1990 to 2015.

^{[3]}5 pollution, 30 provinces in China (a representative emerging economy) from 2007 to 2016 were taken as examples, and threshold regression model and geographically temporally weighted regression model were used to explore the nonlinear relationship and their spatio-temporal heterogeneity.

^{[4]}At the regional level, this paper decomposes the decoupling index into eight influencing factors, and employs Geographically Temporally Weighted Regression (GTWR) to investigate the spatial and temporal heterogeneity of the influencing factors in each region.

^{[5]}

本研究使用地理时间加权回归 (GTWR)，从空间异质性的角度探讨 RETI 如何影响工业污染。

^{[1]}地理时间加权回归（GTWR）用于分析中国生态文明绩效的决定因素。

^{[2]}nan

^{[3]}nan

^{[4]}在区域层面，本文将解耦指数分解为8个影响因素，并采用地理时间加权回归（GTWR）研究各区域影响因素的时空异质性。

^{[5]}

## Multifractal Temporally Weighted 多重分形时间加权

Multifractal temporally weighted detrended cross-correlation analysis (MF-TWXDFA) was proposed to improve the shortcomings of MFDCCA.^{[1]}In order to better study the time series of such cases, we extend the multifractal temporally weighted detrended cross-correlation analysis (MF-TWXDFA) proposed by our group (Wei et al.

^{[2]}

提出了多分形时间加权去趋势互相关分析（MF-TWXDFA）以改善MFDCCA的缺点。

^{[1]}为了更好地研究此类案例的时间序列，我们扩展了我们组（Wei et al.

^{[2]}

## temporally weighted regression 时间加权回归

An analysis framework integrated with ordinary least squares (OLS) and geographically and temporally weighted regression (GTWR) models is proposed to explore the spatiotemporal relationships between urban vibrancy and POI-based variables.^{[1]}A regression model of the incidence of HFMD and climate factors was established based on a geographically and temporally weighted regression (GTWR) model and a generalized additive model (GAM).

^{[2]}(4) The geographically and temporally weighted regression (GTWR) analysis showed that the fitting degree of the new decoupling index is much higher than that of the original decoupling index.

^{[3]}Given the representativeness of land-use change in the loess hilly and gully region (LHGR) was taken as a case study, and ArcGIS spatial analysis techniques and geographically and temporally weighted regression model (GTWR) were used to detect the spatio-temporal differentiation pattern and influencing factors.

^{[4]}In stage two, we used a non-negative geographically and temporally weighted regression method to aggregate the selected base learner predictions based on their local performance.

^{[5]}It then investigates the effects of the COVID-19 time-dependent and basic social vulnerability factors on COVID-19 death rates using geographically and temporally weighted regression (GTWR).

^{[6]}Therefore, a multiscale geographically and temporally weighted regression (MGTWR) model was proposed by specifying a unique bandwidth for each covariate.

^{[7]}Based on the panel data derived from 17,457 observations on new energy enterprises in 29 Chinese provinces during 1998 and 2013 (latest data available), this paper uses data envelopment analysis (DEA) and geographically and temporally weighted regression (GTWR) for the first time to investigate the spatiotemporal characteristics and driving factors of the technical efficiency of China’s NEI.

^{[8]}Using the Geographically Temporally Weighted Regression (GTWR), this study explores how RETI affects industrial pollution from a spatial heterogeneity perspective.

^{[9]}Based on component elements and structural characteristics of urban ecosystem, we use Geographically and Temporally Weighted Regression (GTWR) to analyze spatial and temporal pattern and influencing factors of ECC of 286 cities in China during 2008–2017.

^{[10]}5 and PM10) in China using the improved geographically and temporally weighted regression (IGTWR) model and Fengyun (FY-4A) aerosol optical depth (AOD) data.

^{[11]}This study employed the random forest model and a geographically and temporally weighted regression model (GTWR) in order to analyze the varying importance and spatiotemporal differentiation of the factors influencing ecosystem services in the China's Pearl River Delta (PRD) from 2000 to 2015.

^{[12]}The spatially varying coefficients of Geographically and Temporally Weighted Regression models were used to reveal the empirical relationships between land types and the SRI.

^{[13]}From a statistical point of view, this study respectively provided evidence for the existence of spatial non-stationarity and temporal non-stationarity in winter wheat yield prediction based on geographically weighted regression (GWR) and temporally weighted regression (TWR).

^{[14]}It models the relationship between the dynamic population distribution and the urban built environment using geographically and temporally weighted regression (GTWR), which can account for spatial and temporal non-stationarity simultaneously.

^{[15]}In terms of methodology, these evaluation models are subsequently combined with CCDM (Coupling coordination degree model) and GTWR (Geographically and Temporally Weighted Regression) models to measure and analyze coupling degree and spatio-temporal heterogeneity of UAEE.

^{[16]}Then, the Geographically and Temporally Weighted Regression (GTWR) model analyzes the factors influencing EWP.

^{[17]}This paper selected the relevant data of China's thirty administrative regions from 2005 to 2016, and constructed a geographically and temporally weighted regression model of technological progress and energy intensity, to fully analyze the heterogeneous impact of technological progress on energy intensity.

^{[18]}This study used a geographically and temporally weighted regression model (GTWR) to examine the spatiotemporally heterogeneous impacts of socioeconomic factors on urban land expansion in China using a newly available annual urban land-use dataset from 2000 to 2015.

^{[19]}5 and PM10 in mainland China were estimated by using the Geographically and Temporally Weighted Regression model and FY-4 AOD data.

^{[20]}Geographically Temporally Weighted Regression (GTWR) is used to analyze determinants of the Chinese ecological civilization performance.

^{[21]}Thus, we developed a novel ensemble model named extreme gradient boosting coupled with geographically and temporally weighted regression (XGBoost-GTWR) to predict the high-resolution sulfate concentration (0.

^{[22]}The parameters of the functions that describe the behavior of the housing market are estimated through applying different types of statistical models, including ordinary least squares (OLS), geographically weighted regression (GWR) and geographically and temporally weighted regression (GTWR).

^{[23]}Therefore, in viewing of this, this study integrated coupling coordination degree model (CCDM) and geographically and temporally weighted regression (GTWR) to measure the interaction relationship and spatiotemporal heterogeneity between urbanization and ecosystem health (UAEH) in Chongqing at the county scale from 1997 to 2015.

^{[24]}We applied a geographically and temporally weighted regression (GTWR) to analyze the spatiotemporal pattern of community stay-at-home behaviors against social vulnerability indicators at the census tract level in New York City from March to August, 2020.

^{[25]}This model combined geographically and temporally weighted regression with spatiotemporal kriging and achieved robust prediction performance with sample-based and site-based cross-validation R2 values of 0.

^{[26]}Geographically and temporally weighted regression (GTWR) is a method applied when there is spatial and temporal diversity in the observation.

^{[27]}Here, we used the newly developed monthly water map datasets, the climate dataset from the ERA5 reanalysis, hydrological datasets from Gravity Recovery and Climate Experiment (GRACE) and Global Land Data Assimilation System (GLDAS), the Pearson correlation analysis, and a geographically and temporally weighted regression to characterize the spatial-temporal dynamics of the lakes in Xinjiang from 2000 to 2019 and further to explore their response to climate factors.

^{[28]}ABSTRACT Geographically weighted regression (GWR) and geographically and temporally weighted regression (GTWR) are classic methods for estimating non-stationary relationships.

^{[29]}We used geographic detectors and a geographically temporally weighted regression model (GTWR) to explore the rural settlements’ evolution and driving mechanism in Hubei Province from 1990 to 2015.

^{[30]}To solve this problem, we developed a two-step integrated method to: (i) estimate the 10-km LST with a high spatial coverage from passive microwave (PMW) data using the multilayer perceptron (MLP) model; and (ii) downscale the LST to 1 km and fill the gaps based on the geographically and temporally weighted regression (GTWR) model.

^{[31]}Additionally, we employ the extended STIRPAT (stochastic impacts by regression on population, affluence and technology) and GTWR (geographically and temporally weighted regression) model to reveal the influence of driving factors on CEI from spatial-temporal perspectives.

^{[32]}The geographically and temporally weighted regression (GTWR) model was applied to model the spatiotemporal association between COVID‐19 epidemic and population mobility.

^{[33]}Lastly, the geographically and temporally weighted regression model is applied to assess the spatial–temporal heterogeneity of the correlation between HSR and economic development from a local perspective.

^{[34]}To help bridge this gap, this study takes the sample commercial land prices in the main urban area of Hangzhou from 2006 to 2015 as the empirical research object and investigates the spatiotemporal evolution mechanism of urban commercial land prices through a comparative analysis of the multiple regression analysis (MRA) with ordinary least squares (OLS), the geographically weighted regression (GWR), the temporally weighted regression (TWR), and the geographically and temporally weighted regression (GTWR) models.

^{[35]}Geographically and temporally weighted regression was used to explore the local effect mechanisms.

^{[36]}5 pollution, from the point of view of socioeconomic, this paper uses the geographically and temporally weighted regression (GTWR) model and the latest available data of PM2.

^{[37]}The Geographically and Temporally Weighted Regression (GTWR) model is a development of the Geographically Weighted Regression (GWR) model.

^{[38]}In this paper, we propose an improved framework to explore how individual factors unevenly affect public transport demand over space and time using a geographically and temporally weighted regression (GTWR) model.

^{[39]}ABSTRACT Geographically and temporally weighted regression (GTWR) has been demonstrated as an effective tool for exploring spatiotemporal data under spatial and temporal heterogeneity.

^{[40]}The results revealed that the geographically and temporally weighted regression (GTWR) model performed best with lower AIC values.

^{[41]}In this study, 30 provinces with different population sizes and in different stages of development in China, were selected to explore the spatial heterogeneity of carbon emissions by exploratory spatial data analysis (ESDA), combined with geographically and temporally weighted regression (GTWR).

^{[42]}Specifically, Geographically and Temporally Weighted Regression (GTWR) models for the localization of SPFs were developed.

^{[43]}A geographical and temporally weighted regression is used to test the spatial effects of the RUoEL on the evolution of the ELR patterns.

^{[44]}To comprehensively understand the law of urban-rural relationship and propose scientific measures of urban-rural coordinated development in Northeast China, this study uses the coupling coordination degree model and geographically and temporally weighted regression (GTWR) model to analyze the spatial-temporal patterns and the corresponding driving mechanisms of its urban-rural coordination since 1990.

^{[45]}Geographically and temporally weighted regression (GTWR) is a model that is used to deal with instability in data both spatially and temporally and to produce local parameters.

^{[46]}Taking 110 cities in the Yangtze River Economic Belt (YREB) as the sample, this paper explores the driving mechanism of urbanization development during 2007–2016 by using Geographically and Temporally Weighted Regression Model (GTWR).

^{[47]}Geographically and temporally weighted regression (GTWR) was used to address the spatiotemporal nonlinearity and nonstationarity of climatic drivers.

^{[48]}5 pollution, 30 provinces in China (a representative emerging economy) from 2007 to 2016 were taken as examples, and threshold regression model and geographically temporally weighted regression model were used to explore the nonlinear relationship and their spatio-temporal heterogeneity.

^{[49]}In this paper, the bootstrap test in the geographically weighted regression (GWR) literature is extended to geographically and temporally weighted regression (GTWR) models for identifying homogeneous explanatory variables and spatiotemporally heterogeneous ones.

^{[50]}

提出了一个结合普通最小二乘（OLS）和地理和时间加权回归（GTWR）模型的分析框架，以探索城市活力与基于POI的变量之间的时空关系。

^{[1]}基于地理时间加权回归（GTWR）模型和广义加性模型（GAM）建立手足口病发病率与气候因素的回归模型。

^{[2]}(4)地理和时间加权回归(GTWR)分析表明，新脱钩指数的拟合度远高于原脱钩指数。

^{[3]}鉴于黄土丘陵沟壑区（LHGR）土地利用变化的代表性，以ArcGIS空间分析技术和地理时间加权回归模型（GTWR）检测时空分异格局和影响因素。

^{[4]}在第二阶段，我们使用非负的地理和时间加权回归方法来聚合基于其本地性能的选定基础学习器预测。

^{[5]}然后，它使用地理和时间加权回归 (GTWR) 研究 COVID-19 时间相关和基本社会脆弱性因素对 COVID-19 死亡率的影响。

^{[6]}因此，通过为每个协变量指定唯一的带宽，提出了多尺度地理和时间加权回归 (MGTWR) 模型。

^{[7]}本文基于 1998 年和 2013 年对中国 29 个省份的 17457 家新能源企业观察得出的面板数据（可获得的最新数据），首次使用数据包络分析（DEA）和地理时间加权回归（GTWR）研究中国NEI技术效率的时空特征及驱动因素。

^{[8]}本研究使用地理时间加权回归 (GTWR)，从空间异质性的角度探讨 RETI 如何影响工业污染。

^{[9]}基于城市生态系统的组成要素和结构特征，我们使用地理和时间加权回归（GTWR）分析了2008-2017年中国286个城市ECC的时空格局和影响因素。

^{[10]}5 和 PM10) 在中国使用改进的地理和时间加权回归 (IGTWR) 模型和风云 (FY-4A) 气溶胶光学深度 (AOD) 数据。

^{[11]}本研究采用随机森林模型和地理时间加权回归模型（GTWR），分析2000-2015年中国珠三角（PRD）生态系统服务影响因素的重要性和时空分异。

^{[12]}地理和时间加权回归模型的空间变化系数用于揭示土地类型与 SRI 之间的经验关系。

^{[13]}从统计学的角度，本研究分别为基于地理加权回归（GWR）和时间加权回归（TWR）的冬小麦产量预测存在空间非平稳性和时间非平稳性提供了证据。

^{[14]}它使用地理和时间加权回归 (GTWR) 对动态人口分布与城市建成环境之间的关系进行建模，可以同时考虑空间和时间的非平稳性。

^{[15]}在方法论上，这些评估模型随后与CCDM（耦合协调度模型）和GTWR（地理和时间加权回归）模型相结合，测量和分析UAEE的耦合度和时空异质性。

^{[16]}然后，地理和时间加权回归 (GTWR) 模型分析了影响 EWP 的因素。

^{[17]}本文选取中国30个行政区域2005-2016年的相关数据，构建技术进步与能源强度的时空加权回归模型，全面分析技术进步对能源强度的异质性影响。

^{[18]}本研究使用地理和时间加权回归模型 (GTWR)，利用 2000 年至 2015 年最新可用的年度城市土地利用数据集，研究社会经济因素对中国城市土地扩张的时空异质影响。

^{[19]}采用地理和时间加权回归模型和 FY-4 AOD 数据估计中国大陆的 5 和 PM10。

^{[20]}地理时间加权回归（GTWR）用于分析中国生态文明绩效的决定因素。

^{[21]}因此，我们开发了一种新的集成模型，称为极端梯度提升与地理和时间加权回归 (XGBoost-GTWR) 相结合，以预测高分辨率硫酸盐浓度 (0.

^{[22]}描述房地产市场行为的函数参数是通过应用不同类型的统计模型来估计的，包括普通最小二乘法（OLS）、地理加权回归（GWR）和地理和时间加权回归（GTWR）。

^{[23]}因此，有鉴于此，本研究将耦合协调度模型（CCDM）与地理时空加权回归（GTWR）相结合，测算1997年以来重庆市县域城市化与生态系统健康（UAEH）的交互关系和时空异质性。到 2015 年。

^{[24]}我们应用地理和时间加权回归 (GTWR) 来分析 2020 年 3 月至 8 月纽约市人口普查区一级的社区居家行为与社会脆弱性指标的时空模式。

^{[25]}该模型将地理和时间加权回归与时空克里金法相结合，并在基于样本和基于站点的交叉验证 R2 值为 0 时实现了稳健的预测性能。

^{[26]}地理和时间加权回归 (GTWR) 是一种在观测存在空间和时间多样性时应用的方法。

^{[27]}在这里，我们使用了新开发的月度水图数据集、来自 ERA5 再分析的气候数据集、来自重力恢复和气候实验 (GRACE) 和全球陆地数据同化系统 (GLDAS) 的水文数据集、皮尔逊相关分析以及地理和时间加权回归表征新疆湖泊2000-2019年的时空动态，并进一步探讨其对气候因素的响应。

^{[28]}nan

^{[29]}nan

^{[30]}为了解决这个问题，我们开发了一种两步集成方法：（i）使用多层感知器（MLP）模型从无源微波（PMW）数据估计具有高空间覆盖的 10 公里 LST； (ii) 将 LST 缩小到 1 公里，并根据地理和时间加权回归 (GTWR) 模型填补空白。

^{[31]}此外，我们采用扩展的 STIRPAT（人口、富裕和技术回归的随机影响）和 GTWR（地理和时间加权回归）模型从时空角度揭示驱动因素对 CEI 的影响。

^{[32]}应用地理和时间加权回归 (GTWR) 模型来模拟 COVID-19 流行与人口流动之间的时空关联。

^{[33]}最后，应用地理和时间加权回归模型从地方角度评估高铁与经济发展之间相关性的时空异质性。

^{[34]}nan

^{[35]}使用地理和时间加权回归来探索局部效应机制。

^{[36]}5 污染，从社会经济的角度，本文使用地理和时间加权回归（GTWR）模型和 PM2.5 的最新可用数据。

^{[37]}nan

^{[38]}在本文中，我们提出了一个改进的框架，使用地理和时间加权回归 (GTWR) 模型来探索个体因素如何在空间和时间上不均匀地影响公共交通需求。

^{[39]}摘要 地理和时间加权回归（GTWR）已被证明是在空间和时间异质性下探索时空数据的有效工具。

^{[40]}结果表明，地理和时间加权回归 (GTWR) 模型在 AIC 值较低的情况下表现最佳。

^{[41]}本研究选取中国30个不同人口规模、不同发展阶段的省份，采用探索性空间数据分析（ESDA）结合地理时间加权回归（GTWR）的方法，探讨碳排放的空间异质性。

^{[42]}具体而言，开发了用于 SPF 本地化的地理和时间加权回归 (GTWR) 模型。

^{[43]}地理和时间加权回归用于测试 RUoEL 对 ELR 模式演变的空间影响。

^{[44]}nan

^{[45]}地理和时间加权回归 (GTWR) 是一种用于处理数据在空间和时间上的不稳定性并生成局部参数的模型。

^{[46]}以长江经济带（YREB）110个城市为样本，运用地理和时间加权回归模型（GTWR）探讨2007-2016年城镇化发展的驱动机制。

^{[47]}地理和时间加权回归 (GTWR) 用于解决气候驱动因素的时空非线性和非平稳性。

^{[48]}nan

^{[49]}nan

^{[50]}

## temporally weighted two 时间加权二

Based on the spatiotemporally weighted two-step method (STW-TSM), the spatiotemporal characteristics of the residual microwave brightness temperature (MBT) with the Mw7.^{[1]}By employing the spatio-temporally weighted two-step method (STW-TSM) and microwave brightness temperature (MBT) data from AMSR-2 instrument on board Aqua satellite, this paper investigates carefully the spatiotemporal features of multi-frequency MBT anomalies relating to the earthquake.

^{[2]}A spatio-temporally weighted two-step method (STW-TSM) is developed to retrieve or to mine seismicity-related microwave brightness temperature (MBT) anomaly with microwave satellite big data from Aqua AMSR-E and/or FY-3B MWRI sensors.

^{[3]}

基于时空加权两步法（STW-TSM），剩余微波亮温（MBT）与Mw7的时空特征。

^{[1]}本文利用Aqua卫星上AMSR-2仪器的时空加权两步法（STW-TSM）和微波亮温（MBT）数据，仔细研究了多频MBT异常的时空特征。地震。

^{[2]}开发了一种时空加权两步法（STW-TSM），利用来自 Aqua AMSR-E 和/或 FY-3B MWRI 传感器的微波卫星大数据反演或挖掘与地震活动相关的微波亮温（MBT）异常。

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## temporally weighted detrended 时间加权去趋势

Multifractal temporally weighted detrended cross-correlation analysis (MF-TWXDFA) was proposed to improve the shortcomings of MFDCCA.^{[1]}In order to better study the time series of such cases, we extend the multifractal temporally weighted detrended cross-correlation analysis (MF-TWXDFA) proposed by our group (Wei et al.

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提出了多分形时间加权去趋势互相关分析（MF-TWXDFA）以改善MFDCCA的缺点。

^{[1]}为了更好地研究此类案例的时间序列，我们扩展了我们组（Wei et al.

^{[2]}