共查询到19条相似文献，搜索用时 109 毫秒
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从气候波动的瞬时频率与瞬时振幅出发,结合最小二乘支持向量机技术,提出了基于幅频分离技术的气候时间序列预测方法,并对南京地区降水距平进行了30候的预测试验。结果表明,幅频分离预测法能够对所有模态的振幅和高频模态的瞬时频率进行较好的预测,而预测的瞬时频率累积误差会对模态分量的预测距平相关性产生敏感影响,该新方法能够显著提高气候序列高频模态的预测效果。对于气候序列的低频模态分量,集合经验模态分解的边界效应会对瞬时频率的求解产生较大误差,使得序列边界区的幅角计算不准确,导致对低频模态的最终预测效果不理想。对气候序列的高频分量采用幅频分离并进行最小二乘支持向量机预测,而对其低频分量仅采用最小二乘支持向量机进行直接预测,可同时提高高、低频分量的预测效果,并最终提高整个气候序列的预测准确性。该分频预测方法可以使南京降水预测的30候距平相关保持在0.4以上。 相似文献
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用时间序列的主成分叠加作大板地区年降水量预报秦俊义（巴林右旗气象局）１基本原理及制作思路大气运动中的许多过程常不满足平稳性，而是非平稳的。这就给气象预报带来很大困难。对一个非平稳的时间序列做出预测，关键是对非平稳的时间序列做平稳化处理。将一类非平稳过... 相似文献
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气候系统是一种耗散的、具有多个不稳定源的非线性、非平稳系统。该文利用支持向量机(SVM)算法在处理非线性问题中的优越性和经验模态分解(EMD)算法在处理非平稳信号中的优势,采用将EMD与SVM相结合的短期气候预测方法,并应用到广西季节降水预报中。选取广西88个气象观测站1957—2005年6—8月逐年降水量的距平百分率序列作为试验数据,通过EMD算法将标准化处理后的距平百分率序列分解成多个本征模态函数(IMF)分量和一个趋势分量,在分解中针对EMD算法存在的端点极值问题选择两种方法分别进行处理,对比得出极值延拓法效果更好。对每个分量构建不同的SVM模型进行预测,并通过重构形成最后的预测结果。试验中采用不经EMD处理的反向传播(BP)神经网络和SVM算法进行对比验证,结果表明:相对于直接预测方法,该文提出的方案均方误差最小,能够较为准确地反映出降水序列未来几年的变化趋势,具有更高的预测精度和较好的推广前景。 相似文献
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慢特征分析方法(Slow Feature Analysis,SFA)是从已知的非平稳时间序列中提取缓变信息的有效方法。本文首先通过Logistic非平稳时间序列模型对SFA方法提取慢特征信息的能力进行了检验,然后以哈尔滨市为黑龙江省代表站,对月气温距平序列进行慢特征信号提取及预测研究。结果表明:慢特征分析方法可以有效地提取哈尔滨市气温距平序列中的慢特征信号。提取的慢特征信号能够反映原序列的变化趋势、极值等信息。拟合和预测试验表明,与平稳性模型相比,引入SFA信号后的气温预测模型可以在一定程度上提高预测能力,改善预测效果。对近48个月独立样本预测也得到相同结论。 相似文献
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支持向量机是近年来提出的一种机器学习新算法,采用结构风险最小化准则,把学习问题转化为一个二次规划问题来获得最优解,克服了BP神经网络方法中无法避免的局部极值问题。根据支持向量机的原理和方法,在介绍半干旱半湿润地区汾河水库上游流域自然及水文特性的基础上,建立了基于支持向量机的径流预测模型,利用1990-2005年的水文资料进行了检验,并与BP神经网络预测结果进行了对比,结果表明,支持向量机预报模型的精度比BP神经网络有提高,且月径流和非汛期日径流中的预报结果可以用于指导实践。 相似文献
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本文从时间序列的基本概念出发，阐明了两类不同的分析预测方法。着重讲述了非平稳时间序列的平稳化及P阶自回归模型。并以西宁站夏季降水资料为例讲述了P阶自回归模型的建模步骤。 相似文献
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蔡仁 《沙漠与绿洲气象(新疆气象)》2014,8(3):61-67
选用2012年11月1日-2013年1月31日的逐6 h的空气污染物（SO2、NO2、PM10）和实况气象要素（温度、湿度、能见度、风速和气压）资料,利用支持向量机和Elman神经网络方法建立空气污染物预报模型。结果表明,支持向量机和Elman神经网络方法都可以得到较为理想的预测结果,支持向量机在泛化能力方面具有显著优势,预测结果更加准确。 相似文献
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The Prediction of Non-stationary Climate Series Based on Empirical Mode Decomposition 总被引：1，自引：0，他引：1
This paper proposes a new approach which we refer to as ``segregated
prediction" to predict climate time series which are nonstationary. This
approach is based on the empirical mode decomposition method (EMD), which
can decompose a time signal into a finite and usually small number of basic
oscillatory components. To test the capabilities of this approach, some
prediction experiments are carried out for several climate time series. The
experimental results show that this approach can decompose the
nonstationarity of the climate time series and segregate nonlinear
interactions between the different mode components, which thereby is able to
improve prediction accuracy of these original climate time series. 相似文献
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Summary Most of the stochastic prediction methods are developed for stationary time series. However, many climatic series show clear evidence of non-stationarity. In such cases, methods based on the stationarity assumptions would be inappropriate. Alternative methods such as those based on stochastic approximation are preferable in these cases because they are based on adaptive learning principles. These methods have not been applied and their suitability not tested with nonstationary climatic time series.In the stochastic approximation method, the deterministic component of a nonstationary time series is estimated by first predicting the two steps ahead value of a time series. The two steps-ahead forecast may involve a term characterizing the trend in the time series. The two steps-ahead predictor is corrected to obtain the one step ahead prediction by using a gain sequence.The dynamic stochastic approximation method is used herein to predict non-stationary climatic time series. Daily minimum temperature series at West Lafayette, Indiana, U.S.A. and seasonal temperature and precipitation series at Evansville, Indiana, U.S.A. are used in the study. For data trends, an improved dynamic stochastic approximation method, called the modified dynamic stochastic approximation method gives more accurate predictions. If the method is used for seasonal data, then it can be used to track the time varying mean value.With 6 Figures 相似文献
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Almost all climate time series have some degree of nonstationarity due to external forces of the observed system. Therefore, these external forces should be taken into account when reconstructing the climate dy- namics. This paper presents a novel technique in predicting nonstationary time series. The main difference of this new technique from some previous methods is that it incorporates the driving forces in the pre- diction model. To appraise its effectiveness, three prediction experiments were carried out using the data generated from some known classical dynamical models and a climate model with multiple external forces. Experimental results indicate that this technique is able to improve the prediction skill effectively. 相似文献
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Most real-world time series have some degree of nonstationarity due to external perturbations of the observed system; external driving forces are the essential reason that leads to the nonstationarity of dynamics system. In this paper, the authors present a novel technique in which the authors incorporate external forces to predict nonstationary time series. To test the effect, the authors also examined two prediction experiments with an ideal time series from a logistic map and a proxy climate dataset for the past millennium. The preliminary results show that the resulting algorithm has better predictive ability than the one that does not consider the external forces. 相似文献
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应用自激励门限自回归模式对旱涝游程序列的模拟和预报 总被引：3，自引：0，他引：3
在用AR、ARMA等线性模式对气候序列进行拟合和预报时,由于气候序列中存在着非线性变化,所以拟合和预报效果往往不太理想。本文首次用非线性自激励门限自回归模式(SETAR)对由北京511年(1470—1980年)历史旱涝记录变换的湿涝(干旱)游程记录进行了模拟和预报,解决了长期以来预报方程不能随转折点变更的问题。拟合和预报结果表明:门限自回归模式的拟合和预报效果比线性AR模式有明显提高。AR模式只能预报出2年长度以下的游程转折点,而SETAR模式能较准确地预报出3年长度以上的游程转折点。这可能是因为在预报过程中SETAR模式能按游程转折点更新模式,而且模式建立时不要求序列具有平稳性的缘故。 相似文献
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MODELING AND PREDICTION CONCERNING TIME SERIES OF FLOOD/DROUGHT RUNS USING THE SELF-EXCITING THRESHOLD AUTOREGRESSIVE MODEL 
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下载免费PDF全文 When linear regressive models such as AR or ARMA model are used for fitting and predicting climatic timeseries,results are often not sufficiently good because nonlinear variations in the time series.In this paper,a nonlinear self-exciting threshold autoregressive(SETAR)model is applied to modeling and predicting the timeseries of flood/drought runs in Beijing,which were derived from the graded historical flood/drought records inthe last 511 years(1470—1980).The results show that the modeling and predicting with the SETAR modelare much better than that of the AR model.The latter can predict the flood/drought runs with a length onlyless than two years,while the formal can predict more than three-year length runs.This may be due to thefact that the SETAR model can renew the model according to the run-turning points in the process of predic-tion,though the time series is nonstationary. 相似文献
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Shuoben Bi Shengjie Bi Xuan Chen Han Ji Ying Lu 《Asia-Pacific Journal of Atmospheric Sciences》2018,54(4):611-622
Observed climate data are processed under the assumption that their time series are stationary, as in multi-step temperature and precipitation prediction, which usually leads to low prediction accuracy. If a climate system model is based on a single prediction model, the prediction results contain significant uncertainty. In order to overcome this drawback, this study uses a method that integrates ensemble prediction and a stepwise regression model based on a mean-valued generation function. In addition, it utilizes empirical mode decomposition (EMD), which is a new method of handling time series. First, a non-stationary time series is decomposed into a series of intrinsic mode functions (IMFs), which are stationary and multi-scale. Then, a different prediction model is constructed for each component of the IMF using numerical ensemble prediction combined with stepwise regression analysis. Finally, the results are fit to a linear regression model, and a short-term climate prediction system is established using the Visual Studio development platform. The model is validated using temperature data from February 1957 to 2005 from 88 weather stations in Guangxi, China. The results show that compared to single-model prediction methods, the EMD and ensemble prediction model is more effective for forecasting climate change and abrupt climate shifts when using historical data for multi-step prediction. 相似文献