共查询到18条相似文献,搜索用时 500 毫秒
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逐步门限自回归模型及其建模方案 总被引:2,自引:0,他引:2
针对门限自回归模型在模型识别方面的不足,提出了逐步门限自回归模型,并同时给出了该模型的一种建模方案。数值实例表明,逐步门限自回归模型在模拟和预报稳定上比一般门限自回归模型有一定程度的提高。 相似文献
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门限混合回归模型是由门限自回归模型发展而来。门限自回归模型(Threshold Autore-gressive Model)是由汤家豪于1978年首先提出的,该模型经过国内十几年的研究已经有了很大进展和完善。本文介绍门限混合回归模型在辽宁省一些地区季降水和温度预报中的应用。 相似文献
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1 引言 门限混合回归模型是由门限自回归模型发展而来。门限自回归模型(Threshold Autoregressive Model)是由汤家豪于1978年首先提出的,该模型经过国内十几年的研究已经有了很大进展和完善。本文介绍门限混合回归模型在辽宁省一些地区季降水和温度预报中的应用。 相似文献
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非线性门限自回归模型用于时间序列的外推预报 总被引:3,自引:0,他引:3
对一些呈非线性变化的时间序列,如果勉强用线性统计模型来描述,效果往往不理想。本文利用非线性的自激励门限自回归模型(SETAR)、开环门限自回归模型(TARSO)对我省记录年代较长的烟台年降水量序列进行建模分析,并探讨分析了模型的稳定性,最后利用稳定的SETAR模型进行外推预报。 相似文献
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用多元混合门限回归进行汛期降水量预测试验 总被引:2,自引:0,他引:2
在综合考虑预测对象的周期变化和前期外部因子的共同作用后,给出带有周期分量的多元混合门限回归模型。通过青岛汛期降雨量的7年预报试验表明,该模型具有较为稳定的预报能力,值得进一步研究应用。 相似文献
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南宁市空气臭氧污染的TAR模型预报研究 总被引:1,自引:0,他引:1
以门限自回归统计方法为基础进行南宁城市臭氧日最大小时浓度TAR模型预报研究.选取日平均气压作为参与预报气象因子、180日作为建模资料长度、开方形式的数据转换后建立模型预报效果更佳.预报结果检验表明预报方案是可行的,并提出了改进建议. 相似文献
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考虑了气候系统中一些变量突变时对预测关系的改变作用,用多元门限回归模型的建模方法建立长江下游地区夏季旱涝趋势预测模型。拟合效果较理想,用1994-1995年的独立资料检验,预测结果与实况较为接近。 相似文献
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利用门限回归模型和非线性滑动回归模型,分析天津市2014-2019年日用电量和气温之间的非线性关系,并计算得到不同响应关系下的阈值气温。结果表明:天津地区的阈值气温在不同响应关系下存在明显差异,在"V"型非线性响应关系下,阈值气温为18.8℃,在"U"型响应关系下,线性不对称模型的舒适区范围为12.3-23.4℃,非线性模型的舒适区范围为13.7-21.7℃;对比不同模型的预测效果,认为"U"型优于"V"型模型,非线性模型优于线性模型。对阈值气温的影响要素分析表明,相对湿度对舒适区与冷却区的阈值气温影响较大,阈值气温在相对湿度为30%-50%时较相对湿度为50%-70%时偏大2.2℃,相比之下,相对湿度对舒适区与加热区的阈值气温影响不大;天津地区阈值气温会随时间发生明显变化,2002-2005年的舒适区范围较2014-2019年偏大1.4℃。在实践应用中,应根据模型的需求,并充分考虑相对湿度和时间变化的影响选择阈值气温,从而提升用电量预测的准确率。 相似文献
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基于NDVI的西藏不同草地类型生物量回归建模分析 总被引:1,自引:0,他引:1
旨在建立西藏地区不同草地类型的NDVI遥感估测模型,利用多元线性回归建立了不同草地类型的鲜草生物量与SPOT/VEGETATION多年平均年最大归一化植被指数(NDVI)、年降水量和年积温等变量的回归估测模型。并分析了所有草地类型的平均鲜草生物量与平均年最大NDVI、平均年降水量等因子的相关关系。结果表明:年降水量是鲜草长势最重要的影响因子,基于NDVI的鲜草生物量多元线性回归模型能很好的拟合草地(R=0.964)、高寒草甸(R=0.959)、高寒荒漠草原(R=0.772)、温性草原(R=0.892)和高寒草原(R=0.797)等草地类型。 相似文献
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I. Matyasovszky 《Theoretical and Applied Climatology》2003,74(1-2):69-75
Summary ?Homogenized monthly and annual mean temperatures for ten locations in Hungary from 1901 to 1999 are analyzed. A principal
component analysis was performed and the first new component containing 94.5% of the total variance has been retained. A linear
regression of this variable on a sea level pressure NAO index results in relatively weak correlations. In order to consider
the trends in both data series, a polynomial of years is added to the regression. After a selection of the optimal polynomial
orders by Akaike’s criteria the correlation coefficients are significantly increased. The Southern Oscillation Index (SO index)
characterizing the El Ni?o – Southern Oscillation (ENSO) is then incorporated in the relationship via a nonlinear, threshold
model. The threshold model consists of the above linear regressions but is conditioned on the SO index threshold variable.
The rationale behind this approach is to allow a change of model performance according to ENSO phase. The thresholds are not
pre-specified but are estimated from the data, while the number of thresholds is chosen by Akaike’s criteria. A likelihood
ratio test shows an improvement of these models over the linear model with very strong significance levels, except in June.
Received March 25, 2002; revised June 20, 2002; accepted June 23, 2002 相似文献
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A timescale decomposed threshold regression(TSDTR) downscaling approach to forecasting South China early summer rainfall(SCESR) is described by using long-term observed station rainfall data and NOAA ERSST data. It makes use of two distinct regression downscaling models corresponding to the interannual and interdecadal rainfall variability of SCESR.The two models are developed based on the partial least squares(PLS) regression technique, linking SCESR to SST modes in preceding months on both interannual and interdecadal timescales. Specifically, using the datasets in the calibration period 1915–84, the variability of SCESR and SST are decomposed into interannual and interdecadal components. On the interannual timescale, a threshold PLS regression model is fitted to interannual components of SCESR and March SST patterns by taking account of the modulation of negative and positive phases of the Pacific Decadal Oscillation(PDO). On the interdecadal timescale, a standard PLS regression model is fitted to the relationship between SCESR and preceding November SST patterns. The total rainfall prediction is obtained by the sum of the outputs from both the interannual and interdecadal models. Results show that the TSDTR downscaling approach achieves reasonable skill in predicting the observed rainfall in the validation period 1985–2006, compared to other simpler approaches. This study suggests that the TSDTR approach,considering different interannual SCESR-SST relationships under the modulation of PDO phases, as well as the interdecadal variability of SCESR associated with SST patterns, may provide a new perspective to improve climate predictions. 相似文献