基于省区协调发展的GDP预测模型

基于柯布-道格拉斯生产函数与自回归移动平均模型(ARIMA)构建出一个GDP综合预测模型,并且考虑十九大全面建成小康社会与实现共同富裕的精神与国家关于技术、资本、劳动力等方面的区域平衡发展战略调整模型的参数,计算了2016—2050年中国分省的GDP总量与人均GDP,进一步通过计算省区间人均GDP的基尼系数来分析省区协调发展的水平。研究结果表明,在考虑省区协调发展时,各省区在2016—2050年间的GDP总量与人均GDP的差距逐渐缩小,省区间人均GDP的基尼系数将从2015年的0.219下降到2030年的0.176和2050年的0.137,未来区域间发展不均衡的态势在实现经济稳步增长同时可以得到缓解。     

基于EEMD和ARIMA的海温预测模型研究

类型丰富、时空分辨率高的海洋探测数据,为信号分解和机器学习算法的应用提供了可能。本文针对如何建立有效的海温预测模型这一问题,使用高时空分辨率的海表温度(SST)融合产品,引入信号处理领域的集合经验模态分解(EEMD)和机器学习领域的自回归积分滑动平均模型(ARIMA)。首先利用最适于分解自然信号的EEMD方法,将海温数据分解成多个确定频率的序列;再利用ARIMA分别对各个频率的序列进行预测,最后将各个序列的预测结果进行组合。该方法在丰富数据的支撑下,比以往直接使用海温数据所建立的预测模型精度更高,为更好地进行海温预测提供了新方法。     

Prediction of the dynamic response of a slender marine structure under an irregular ocean wave using the NARX-based quadratic Volterra series

The time series of the dynamic response of a slender marine structure was predicted in approximate sense using a truncated quadratic Volterra series. The wave-structure interaction system was identified using the NARX (Nonlinear Autoregressive with Exogenous Input) technique, and the network parameters were determined through supervised training using prepared datasets. The dataset used for network training was obtained by nonlinear finite element analysis of the slender marine structure under random ocean waves of white noise. The nonlinearities involved in the analysis were both large deformation of the structure under consideration and the quadratic term of the relative velocity between the water particle and structure in the Morison formula. The linear and quadratic frequency response functions of the given system were extracted using the multi-tone harmonic probing method and the time series of the response of the structure was predicted using the quadratic Volterra series. To check the applicability of the method, the response of a slender marine structure under a realistic ocean wave environment with a given significant wave height and modal period was predicted and compared with the nonlinear time domain simulation results. The predicted time series of the response of structure with quadratic Volterra series successfully captured the slowly varying response with reasonably good accuracy. This method can be used to predict the response of the slender offshore structure exposed to a Morison type load without relying on the computationally expensive time domain analysis, especially for screening purposes.     

BIBLIOGRAPHIE

Abstract

Time series modelling approaches are useful tools for simulating and forecasting hydrological variables and their change through time. Although linear time series models are common in hydrology, the nonlinear time series model, the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model, has rarely been used in hydrology and water resources engineering. The GARCH model considers the conditional variance remaining in the residuals of the linear time series models, such as an ARMA or an ARIMA model. In the present study, the advantages of a GARCH model against a linear ARIMA model are investigated using three classes of the GARCH approach, namely Power GARCH, Threshold GARCH and Exponential GARCH models. A daily streamflow time series of the Matapedia River, Quebec, Canada, is selected for this study. It is shown that the ARIMA (13,1,4) model is adequate for modelling streamflow time series of Matapedia River, but the Engle test shows the existence of heteroscedasticity in the residuals of the ARIMA model. Therefore, an ARIMA (13,1,4)-GARCH (3,1) error model is fitted to the data. The residuals of this model are examined for the existence of heteroscedasticity. The Engle test indicates that the GARCH model has considerably reduced the heteroscedasticity of the residuals. However, the Exponential GARCH model seems to completely remove the heteroscedasticity from the residuals. The multi-criteria evaluation for model performance also proves that the Exponential GARCH model is the best model among ARIMA and GARCH models. Therefore, the application of a GARCH model is strongly suggested for hydrological time series modelling as the conditional variance of the residuals of the linear models can be removed and the efficiency of the model will be improved.

Editor D. Koutsoyiannis; Associate editor C. Onof

Citation Modarres, R. and Ouarda, T.B.M.J., 2013. Modelling heteroscedasticty of streamflow times series. Hydrological Sciences Journal, 58 (1), 1–11.     

陕西果业基地伏旱指数及其预报方法

选取陕西苹果(梨)生产基地县中生态环境、气候特点与苹果(梨)产业发展水平具有代表性的10个台站,近40 a(1969—2008年)伏期降水量和无降水日数资料,设计并计算其伏旱指数。将伏旱指数分成强、偏强、中等、偏弱和弱五个等级,并用典型k阶自回归AR(k)预测模式进行独立样本预测试验。结果表明:伏旱指数能够较客观地反映基地县的伏旱强度,且伏旱指数的时空分布特征差异较大。这与这些基地县所处纬度、海拔高度、生态环境和气候背景的差异有关。典型k阶自回归预测模式预测准确及基本准确率在70%左右,预报效果尚好,具有实用价值。     

Eigendecomposition of TDR waveforms: a novel method to determine water content and pore fluid concentration of sandy soils

In this study, an incident pulse signal of several harmonics (i.e., multiples of the fundamental frequency) was used as a source in the time domain reflectometry (TDR) probing technique. Reflected signals were captured by an oscilloscope and their characteristics were determined via eigendecomposition. Autoregressive modeling and singular value decomposition were used to calculate the eigenvalues and the most significant ones were identified based on power spectrum. A multivariate statistical analysis was performed for the two most dominant eigenvalues, which are dependent on water content and salt concentrations, and regression equations were obtained. To determine the water content and salt concentrations in terms of the first and second eigenvalues, a modified Powell hybrid algorithm was used to solve the obtained system of nonlinear equations. Actual and predicted results are in agreement indicating that the developed method is very successful in predicting water content and salt concentrations. Furthermore, on comparing the eigendecomposition method with Fourier spectral analysis, one can observe that the former is superior in predicting water content and salt concentrations.     

Modelling the biological half-life and seasonality of ¹⁴C in Fucus vesiculosus from the east coast of Ireland: implications for the estimation of future trends

Radiocarbon levels were recorded in Fucus vesiculosus samples collected on a monthly basis over a three-year period at a site on the east coast of Ireland. The resulting data was analysed using a numerical model which estimates the transit times from the Sellafield plant to the sampling location, and the mean availability time of ¹⁴C in seaweed. With the inclusion of a model parameter allowing for seasonal variability in uptake by the Fucus, good correlation was observed between the predicted and measured concentrations. Future temporal trends of ¹⁴C Fucus concentrations along the eastern Irish coastline were modelled with the application of three possible prospective discharge scenarios, predicting ¹⁴C Fucus concentrations to reduce to ambient background levels within 2.5-years of discharges being set to zero. Such projections may prove helpful in assessing the consequences of discharge management and policy making in the context of the OSPAR convention.     

广东降水延伸期预报的探索

利用1961-2005年广东省站逐日降水资料,采用奇异谱分析结合自回归模型对广东候平均降水进行未来1～5候的延伸期预报试验.结果表明,广东省降水侯平均存在一个准40 ～60 d的振荡周期,对1961-2000年候降水重构相关系数在0.96以上.对2001-2005年候降水进行未来1～5候的预报试验,预报效果除2003年后延第5候较差外,其它4 a的预报结果都较好.后延第1候预报相关系数最高达到0.86,后延预报到第5候时相关系数也在0.3以上.从预报平均相对均方根误差来看,5 a均在30％以内,预报的稳定性和效果较理想.总体说明奇异谱分析结合自回归模型是一种较为有效的预报方法,在未来10～30 d延伸期天气预报业务中有一定的参考价值.     

基于预报误差最小的自回归模型定阶方法研究

以独立样本的平均绝对预报误差最小为依据。进行了自回归定型阶方法的研究。简称ＭＡＰＥ方法。应用ＭＡＰＥ方法及五种常用的定阶方法，对上海月平均温度序列进行定阶试验计算和预报检验。结果表明，ＭＡＰＥ方法是一种效果较好的自回归模型定价方法，并且简单实用。     

Robust reconstruction of aliased data using autoregressive spectral estimates

Autoregressive modeling is used to estimate the spectrum of aliased data. A region of spectral support is determined by identifying the location of peaks in the estimated spatial spectrum of the data. This information is used to pose a Fourier reconstruction problem that inverts for a few dominant wavenumbers that are required to model the data. Synthetic and real data examples are used to illustrate the method. In particular, we show that the proposed method can accurately reconstruct aliased data and data with gaps.