搓制钻头的数学模型

车分析搓制钻头工艺．建立了数学模型．并对其进行了几何仿真，得到四板搓的截形数据．给出用一种规格的工具搓制多种规格钻头且符合国标的工具序列数据．     

蕴含W6-可图序列

摘要对于给定的图日,如果可图序列π有一个实现包含日作为子图,则称丌是蕴含H-可图的．本文给出了可图序列π蕴含W6-可图的一个充分条件,其中Wτ是τ个顶点的轮图．     

关于1-序列商映射

引进了1-序列商映射，证明了1-序列商映射象保持sm-第一可数空间．作为这一结果的一个应用，本文证明了几乎开，闭映射保持度量空间，g-度量空间，sm-度量空间．此外本文还证明了度量空间上的1-序列商，紧映射是1-序列覆盖映射．这些结果改进并推广了广义度量空间映射象的有关理论．     

一类季节性整值复合自回归模型-SINCAR

本提出一类季节性整值复合自回归模型-SINCAR，通过升维的方法能将-SINCAR变为多维平稳序列，且给出收益序列的极值，凹凸性条件，对周期为3的AINCAR模型中的参数进行了估计。     

局部可分度量空间的序列覆盖s象

本文给出了局部可分度量空间的1序列覆盖s象,2序列覆盖s象,强序列覆盖s象,序列覆盖s象及子序列覆盖s象的内在刻划．从而使关于对度量空间的各类s象的内在刻划方面的研究更趋于完整．     

基序列与有界线性算子

该文用基序列来刻划Banach空间的几何性质，以及Banach空间中某些特殊算子的特征，得到一些好的结果．如有界线性算子T：X→Y是一个同构的充分必要条件是T在X的每个具有基的子空间上的限制也是一个同构．对紧算子T：c0→X也有众多的刻划．     

图的度序列

图的度序列是图论研究中一个重要的课题．至今已发表了４００余篇文章．本文概述这一课题的某些进展，其中包括了可图序列的判准、蕴含Ｐ可图序列和强迫Ｐ可图序列的一些主要结论，同时列出了一些有待进一步研究的问题．     

一种混沌多相伪随机序列

提出了一种混沌多相伪随机序列生成方法，推导出通过Logistic映射产生独立同分布多相序列的充分条件，即根据混沌轨迹的概率密度分布把混沌吸引子划分为2n个区域，对混沌轨迹进行采样间隔为n的采样，对照轨道点所处位置与相应的序列元素之间的映射关系，可以得到独立、均匀分布的2n相伪随机序列。数值统计分析支持以上研究结果并表明该序列具有较高的复杂度。此外文中给出了该序列生成的快速算法和一般表达式。该序列可用于信息安全、扩频通信等众多领域。      

实现同一度序列的图的最大团数

图G中最大完全子图的阶数称为G的团效．ω(π)和γ(π)分别表示实现度序列π=(d_1,d_2,…,d_n)的图的最大团数和最小团数．Erds,Jacobson和Lehel开始考虑确定具有相同度序列π的图的可能的团数问题．他们证明了对于充分大的n,有ω(π)-γ(π)-n一2n~(2/3)．在本文中,我们首先估计了一类特殊可图序列的ω(π)之值,其次我们建立了一个估计任意可图序列π的ω(π)之值的算法．     

关于局部子图可重构性的一个新结果

本文研究局部子图的可重构性。一个图Ｇ在一顶点ｖ处的ｋ－局部子图是到ｖ距离小于等于ｋ的顶点导出且以ｖ为根的子图，记为ＬＧ＾ｋ（ｖ）。本文通过引进核子图的结构证明了ｋ－局部子图是可重构的，如果每一个ｋ－局部子图所含的顶点数都小于等于│Ｖ（Ｇ）│－１。这个结果改进了原有的结果。由这个新结果可知，图的半径这个参数是可重构的。本文还提出了点距序列的概念，并进一步讨论了点距序列与局部子图的关系和一些未解决的问     

The Application of Kernel Smoothing to Time Series Data

There are already a lot of models to fit a set of stationary time series, such as AR, MA, and ARMA models. For the non-stationary data, an ARIMA or seasonal ARIMA models can be used to fit the given data. Moreover, there are also many statistical softwares that can be used to build a stationary or non-stationary time series model for a given set of time series data, such as SAS, SPLUS, etc. However, some statistical softwares wouldn＇t work well for small samples with or without missing data, especially for small time series data with seasonal trend. A nonparametric smoothing technique to build a forecasting model for a given small seasonal time series data is carried out in this paper. And then, both the method provided in this paper and that in SAS package are applied to the modeling of international airline passengers data respectively, the comparisons between the two methods are done afterwards. The results of the comparison show us the method provided in this paper has superiority over SAS＇s method.     

Grey forecasting method of quarterly hydropower production in China based on a data grouping approach

Grey model GM (1,1) has been widely used in short-term prediction of energy production and consumption due to its advantages in data sets with small numbers of samples. However, the existing GM (1,1) modelling method can merely forecast the general trend of a time series but fails to identify and predicts the seasonal fluctuations. In the research, the authors propose a data grouping approach based grey modelling method DGGM (1,1) to predict quarterly hydropower production in China. Firstly, the proposed method is used to divide an entire quarterly time series into four groups, each of which contains only time series data within the same quarter. Afterwards, by using the new series of four quarters, models are established, each of which includes specific seasonal characteristics. Finally, according to the chronological order, the prediction results of four GM (1,1) models are combined into a complete quarterly time series to reflect seasonal differences. The mean absolute percent errors (MAPEs) of the test set 2011Q1–2015Q4 solved using the DGGM (1,1), traditional GM (1,1), and SARIMA models are 16.2%, 22.1%, and 22.2%, respectively; the results indicated that DGGM (1,1) has better adaptability and offers a higher prediction accuracy. It is predicted that China's hydropower production from 2016 to 2020 is supposed to maintain its seasonal growth with the third and first quarters showing the highest and lowest productions, respectively.     

Separation of random noise models from seasonal time series data

The autocorrelation function of seasonal time series data is shown to have peaks which occur at the correlation lags equal to the integer multiples of the fundamental period that is present in the series. This property is shown to be valid even if some of the harmonics including the fundamental are removed from the time series data. Using this property, an analytical procedure is presented for estimating the variance of the white noise generating the low frequency random walk model present in the data. The procedure is similarly extended to estimate the variance of white noise generating the autoregressive (AR) and moving average (MA) noise models. The method is validated on several seasonal time series data whose components are known a priori.     

逐段非趋势波动分析方法及我国证券市场的实证研究

为了研究金融时间序列在不同时段波动的持久性特征,本文提出了一种逐段非趋势波动分析方法。该方法首先建立多阶段分割动态规划模型对时间序列进行分割,随后对各子序列逐段进行非趋势波动分析。对我国证券市场的七个主要指数的实证研究结果表明,本文提出的逐段非趋势波动分析方法能够有效揭示股票指数持久性特征随时间变化的现象,也有助于更深入的比较不同指数间持久性特征的相似程度。     

Neural network forecasting for seasonal and trend time series

Neural networks have been widely used as a promising method for time series forecasting. However, limited empirical studies on seasonal time series forecasting with neural networks yield mixed results. While some find that neural networks are able to model seasonality directly and prior deseasonalization is not necessary, others conclude just the opposite. In this paper, we investigate the issue of how to effectively model time series with both seasonal and trend patterns. In particular, we study the effectiveness of data preprocessing, including deseasonalization and detrending, on neural network modeling and forecasting performance. Both simulation and real data are examined and results are compared to those obtained from the Box–Jenkins seasonal autoregressive integrated moving average models. We find that neural networks are not able to capture seasonal or trend variations effectively with the unpreprocessed raw data and either detrending or deseasonalization can dramatically reduce forecasting errors. Moreover, a combined detrending and deseasonalization is found to be the most effective data preprocessing approach.     

Parsimonious modelling and forecasting of seasonal time series

Some seasonal time series models are considered which are appropriate for the univariate modelling and forecasting of many time series. The equivalent ARIMA forms of these models provide the basis for a critical examination of the Box-Jenkins approach to seasonal model-building. It is concluded that this approach is unsatisfactory and in particular can often result in over-differencing and the adoption of an inappropriate model. Two main reasons for this are discussed: (a) the inadequate class of models considered which rests on a restricted view of parsimony, and (b) the shortcomings of the basic approach to model identification.     

Forecasting time series with multiple seasonal patterns

A new approach is proposed for forecasting a time series with multiple seasonal patterns. A state space model is developed for the series using the innovations approach which enables us to develop explicit models for both additive and multiplicative seasonality. Parameter estimates may be obtained using methods from exponential smoothing. The proposed model is used to examine hourly and daily patterns in hourly data for both utility loads and traffic flows. Our formulation provides a model for several existing seasonal methods and also provides new options, which result in superior forecasting performance over a range of prediction horizons. In particular, seasonal components can be updated more frequently than once during a seasonal cycle. The approach is likely to be useful in a wide range of applications involving both high and low frequency data, and it handles missing values in a straightforward manner.     

利用加权对称估计量对季节性时间序列的单位根检验

本文提出对季节性时间序列利用加权对称估计量的单位根检验,导出相应统计量的极限分布。用MonteCarlo方法计算经验百分位数及检验势,并对最小平方估计量,简单对称估计量和加权对称估计量的经验检验势作了比较。     

Seasonal clustering technique for time series data

In data mining, the unsupervised learning technique of clustering is a useful method for ascertaining trends and patterns in data. Most general clustering techniques do not take into consideration the time-order of data. In this paper, mathematical programming and statistical techniques and methodologies are combined to develop a seasonal clustering technique for determining clusters of time series data. We apply this technique to weather and aviation data to determine probabilistic distributions of arrival capacity scenarios, which can be used for efficient traffic flow management. In general, this technique may be used for seasonal forecasting and planning.     

Bayesian Semiparametric Regression Analysis of Multicategorical Time-Space Data

We present a unified semiparametric Bayesian approach based on Markov random field priors for analyzing the dependence of multicategorical response variables on time, space and further covariates. The general model extends dynamic, or state space, models for categorical time series and longitudinal data by including spatial effects as well as nonlinear effects of metrical covariates in flexible semiparametric form. Trend and seasonal components, different types of covariates and spatial effects are all treated within the same general framework by assigning appropriate priors with different forms and degrees of smoothness. Inference is fully Bayesian and uses MCMC techniques for posterior analysis. The approach in this paper is based on latent semiparametric utility models and is particularly useful for probit models. The methods are illustrated by applications to unemployment data and a forest damage survey.