小波分析在经济预测模型中的应用

经济信号也是一种时间序列 ,它和小波分析中的信号具有相同的特性 .因此 ,可将经济时间序列看成经济信号 ,应用小波进行实际经济分析和预测 .论文针对最小二乘法的不足 ,提出了多分辨回归分析处理经济数据分析的方法 .本文在建立宏观模型时 ,利用小波分析对经济数据进行预处理 ,获得能反映宏观变化趋势的低频信息 ,再用最小二乘法进行拟合和预测 ,通过对传统最小二乘法建立的模型的对比分析 ,结果表明 :本方法优于一般最小二乘法 .

The Sequential Continuity of Multiplication on a Class of Infinite Matrix Algebras

Let λ and μ are sequence spaces and have both the signed_weak gliding hump property, (λ,μ) be the algebra of the infinite matrix operators which transform λ into μ, in this paper, we study the strong? Mackey? weak multiplier sequentially continuous problem of infinite matrix algebras (λ,μ).

On Smarandache Multiplicative Sequence and Its Generation

The main purpose of this paper is to introduce the general Smarandache mul- tiplicative sequence based on the Smarandache multiplicative sequence, and calculate the value of some infinite series involving these sequences.

Quantile Trends in Temperature Extremes in China

A number of recent studies have examined trends in extreme temperature indices using a linear regression model based on ordinary least-squares. In this study, quantile regression was, for the first time, applied to examine the trends not only in the mean but also in all parts of the distribution of several extreme temperature indices in China for the period 1960–2008. For China as a whole, the slopes in almost all the quantiles of the distribution showed a notable increase in the numbers of warm days and warm nights, and a significant decrease in the number of cool nights. These changes became much faster as the quantile increased. However, although the number of cool days exhibited a significant decrease in the mean trend estimated by classical linear regression, there was no obvious trend in the upper and lower quantiles. This finding suggests that examining the trends in different parts of the distribution of the time-series is of great importance. The spatial distribution of the trend in the 90 th quantile indicated that there was a pronounced increase in the numbers of warm days and warm nights, and a decrease in the number of cool nights for most of China, but especially in the northern and western parts of China, while there was no significant change for the number of cool days at almost all the stations.

FORECASTING TIME SERIES WITH GENETIC PROGRAMMING BASED ON LEAST SQUARE METHOD

Although time series are frequently nonlinear in reality, people tend to use linear models to fit them under some assumptJLons unnecessarily in accordance with the truth, which unsurprisingly leads to unsatisfactory performance. This paper proposes a forecast method： Genetic programming based on least square method （GP-LSM）. Inheriting the advantages of genetic algorithm （GA）, without relying on the particular distribution of the data, this method can improve the prediction accuracy because of its ability of fitting nonlinear models, and raise the convergence speed benefitting from the least square method （LSM）. In order to verify the vMidity of this method, the authors compare this method with seasonal auto regression integrated moving average （SARIMA） and back propagation artificial neural networks （BP-ANN）. The results of empirical analysis show that forecast accuracy and direction prediction accuracy of GP-LSM are obviously better than those of the others.

HASM-AD Algorithm Based on the Sequential Least Squares

The HASM(high accuracy surface modeling) technique is based on the fundamental theory of surfaces,which has been proved to improve the interpolation accuracy in surface fitting.However,the integral iterative solution in previous studies resulted in high temporal complexity in computation and huge memory usage so that it became difficult to put the technique into application,especially for large-scale datasets.In the study,an innovative model(HASM-AD) is developed according to the sequential least squares on the basis of data adjustment theory.Sequential division is adopted in the technique,so that linear equations can be divided into groups to be processed in sequence with the temporal complexity reduced greatly in computation.The experiment indicates that the HASM-AD technique surpasses the traditional spatial interpolation methods in accuracy.Also,the cross-validation result proves the same conclusion for the spatial interpolation of soil PH property with the data sampled in Jiangxi province.Moreover,it is demonstrated in the study that the HASM-AD technique significantly reduces the computational complexity and lessens memory usage in computation.