CPI预测的SVM-ARIMA模型构建与数值模拟

居民消费价格指数（CPI）是宏观经济中的前瞻性指标,为经济政策的制定提供数据支撑,发挥指导作用。文章利用CPI的月度数据构建基于小波分解的SVM-ARIMA组合模型,实现了对CPI的精准预测。首先,对2000—2019年的居民消费价格指数序列进行小波分解;然后,对分解后的居民消费价格指数序列分别利用ARIMA模型和SVM模型进行预测;最后,将预测结果进行整合形成对居民消费价格指数的组合预测模型,并选用2020年的实际CPI月度数据与模型预测数据进行有效性验证。结果表明：组合模型的平均绝对百分比误差（MAPE）与均方根误差（RMSE）分别为0.5383%和0.6604%,相较于ARIMA时间序列模型和SVM模型实现了极大的改进。此外,该组合模型的预测分析框架具有较强的适应性和扩展性,可用于其他相同特征类型的时间序列数据的模拟预测。     

一种改进的近似非齐次指数增长离散灰色预测方法

近似非齐次指数增长离散灰色模型DGM(1,1)解决了原模型的固有偏差问题,但在解决现实中有阶跃扰动、大波动变化的初始序列的时候预测结果依然存在明显的偏差.文章在近似非齐次指数增长离散灰色模型中引入残差,构建偏差修正序列,并以其为初始序列重构预测模型,分情况对预测结果进行修正.通过算例进行比较分析,验证了改进模型的精确性和实用性.     

基于SARIMA模型的我国季度GDP时间序列分析与预测

文章主要研究季节时间序列模型在我国季度GDp时间序列预测中的应用,并分析探讨模型的准确性和实用性.文章分析了我国1992～2008年的季度GDP时间序列,剔除时间趋势和季节性后使原序列平稳并建立季节时间序列模型.通过对不同模型进行参数估计和比较后发现:ARIMA(2,1,1)(1,1,1)4能很好地拟合我国季度GDP时间序列,用该模型进行预测得出了2009年四个季度和2010年前两个季度的GDP数值,分析发现季度GDP仍然呈增长趋势,但其速度放缓.预测结果的准确性较高,并具有一定现实意义.     

Lee-Carter模型外推预测死亡率及偏差纠正

Lee-Carter模型是人口死亡率预测的常用模型,泊松最大似然估计法是该模型参数估计广为采纳的方法,模型中与时间相关的因子可建立时间序列模型并进行外推,进而实现死亡率的预测.由于时间因子与死亡率之间的非线性性,简单的外推会带来死亡率预测的低估偏差.这个偏差可以通过对数正态分布的性质进行纠正或者随机模拟方法进行无偏预测.     

基于ARIMA模型的城市网格化管理预测与仿真

文章对城市网格化管理问题进行了研究,基于网格化问题数据的时间序列特征的归总,利用了标准化时间序列模型ARMA族进行分析,并以AR,MA模型为比较基础,进而对选定的ARIMA模型预测结果进行精度比较测试.研究发现,时间序列模型对城市网格化管理问题的预测精度较高,而网格化管理本身对于城市建设与管理和城市决策的资源整合具有相当重要的意义.     

税收收入预测的时间序列方法选择

税收是国家财政收入的主要来源,能否准确地预测税收收入对于制定国家财政预算具有重要意义.文章以中国2001年至2007年的税收收入数据为基础,分别采用传统时间序列分析方法和Box-Jenkins的方法建立了中国月度税收收入的时间序列预测模型.该模型可用于对未来短期情况的预测,同时说明有时在进行预测时传统方法除了操作简便外,精度也更高一些.因此,在建模时,要通过对几个不同模型的比较,找出数据规律,确定最优的模型.     

我国男女两性老龄人口死亡率联合建模与预测

老龄人口死亡率建模和预测是长寿风险度量和养老金风险管理的基础。在我国,退休年龄及以上老龄人口死亡数据稀少,随机波动大,构建能够捕捉老龄人口死亡率随性别、年龄和时间变动的动态预测模型成为难题。本文采用Logistic两人口死亡率模型研究我国老龄人口死亡率的建模与预测。首先,运用死亡率数据质量较好的我国台湾地区数据,对模型结构进行选择,并检验模型的稳健性和预测性能。其次,基于我国大陆地区死亡率数据对模型结构进行二次验证和选择,应用所选模型对大 陆地区老龄死亡率进行建模和预测。结果显示,对于我国男女老龄死亡率的拟合和预测,Logistic 两人口模型均优于单人口CBD模型。最后,运用Logistic两人口死亡率模型对死亡率在年龄和时间两个维度上外推和预测,计算出时期和队列老龄人口分年龄的预期余寿,为养老金精算评估和长寿风险分析提供更准确的数据支持。     

互联网企业广告收入预测研究——基于低频数据的神经网络和时间序列组合模型

神经网络模型对大样本时间序列的拟合效果优于传统时间序列模型,但对于年度、月度、日度等低频时间序列的预测则难以发挥其优势。鉴于此,本文应用传统时间序列模型和神经网络模型,建立Holtwinters-BP组合模型,利用Holtwinters模型分别拟合各解释变量序列,利用BP模型拟合解释变量和自变量的非线性关系,基于某社交新闻类APP的日广告收入数据进行互联网企业广告收入预测研究。通过与循环神经网络(RNN)模型、长短期记忆神经网络(LSTM)模型等预测结果的对比发现:Holtwinters-BP组合模型的预测精度和稳定性更高;证明多维变量对于广告收入的显著影响,多变量模型的预测准确性高于单变量模型;构建的Holtwinters-BP组合模型对于低频数据预测有较好的有效性和适用性。     

基于混合时间序列模型的粮食产量预测

粮食产量的预测是保障粮食安全的重要组成部分.文章结合河南省许昌市粮食产量的历史数据,首先建立趋势外推预测模型,并对模型进行相应的分析;然后运用趋势外推与ARIMA模型(求和自回归移动平均模型)结合起来的混合时间序列模型对趋势值和真实值之间的离差序列即残差进行分析,得到混合时间序列模型的预测结果;最后通过比较得出的混合时间序列模型预测的精度较高,可作为粮食总产量预测的有效工具之一.     

基于回归方法的灰色包络带预测模型改进

传统灰色包络带预测模型在上(下)界函数构造上有其不足,从而造成对预测精度影响。文章利用回归分析的方法构建上缘点连线的逼近曲线,并由此构建上缘点的序列点的上界函数。利用GM(1,1)模型得到时间响应式,并由时间响应式得到改进包络带预测模型。通过比较传统包络带模型、改进包络带模型和中位线序列模型预测精度,以说明改进的包络带模型在预测精度上得到了显著提高。     

Trends in inequality in infant mortality in the north of England, 1921–1973, and their association with urban and social structure

Summary.  The paper analyses a time series of infant mortality rates in the north of England from 1921 to the early 1970s at a spatial scale that is more disaggregated than in previous studies of infant mortality trends in this period. The paper describes regression methods to obtain mortality gradients over socioeconomic indicators from the censuses of 1931, 1951, 1961 and 1971 and to assess whether there is any evidence for widening spatial inequalities in infant mortality outcomes against a background of an overall reduction in the infant mortality rate. Changes in the degree of inequality are also formally assessed by inequality measures such as the Gini and Theil indices, for which sampling densities are obtained and significant changes assessed. The analysis concerns a relatively infrequent outcome (especially towards the end of the period that is considered) and a high proportion of districts with small populations, so necessitating the use of appropriate methods for deriving indices of inequality and for regression modelling.     

Space–time calibration of radar rainfall data

Motivated by a specific problem concerning the relationship between radar reflectance and rainfall intensity, the paper develops a space–time model for use in environmental monitoring applications. The model is cast as a high dimensional multivariate state space time series model, in which the cross-covariance structure is derived from the spatial context of the component series, in such a way that its interpretation is essentially independent of the particular set of spatial locations at which the data are recorded. We develop algorithms for estimating the parameters of the model by maximum likelihood, and for making spatial predictions of the radar calibration parameters by using realtime computations. We apply the model to data from a weather radar station in Lancashire, England, and demonstrate through empirical validation the predictive performance of the model.     

Infant mortality rates: time trends and fractional integration

This paper examines the existence of time trends in the infant mortality rates in a number of countries in the twentieth century. We test for the presence of deterministic trends by adopting a linear model for the log-transformed data. Instead of assuming that the error term is a stationary I(0), or alternatively, a non-stationary I(1) process, we allow for the possibility of fractional integration and hence for a much greater degree of flexibility in the dynamic specification of the series. Indeed, once the linear trend is removed, all series appear to be I(d) with 0<d<1, implying long-range dependence. As expected, the time trend coefficients are significantly negative, although of a different magnitude from those obtained assuming integer orders of differentiation.     

A Test for Constant Hazard Against a Change-Point Alternative

In this article, we consider the change-point hazard rate model which arises quite commonly in mechanical or biological systems, which experience a high hazard rate early in their lifetime due to infant mortality and then a constant or steady hazard rate after the threshold time. We first derive the corresponding mean residual life function (MRLF) and observe that the MRLF is initially increasing and then constant. Here, we derive a test statistic for exponentiality against Increasing Initially then Constant Mean Residual Life (ICMRL). We also derive the asymptotic distribution of the test statistic and compare the power of the test with other existing tests such as likelihood ratio, Weibull, and Log gamma tests considered in the literature. The test performs quite well as compared to other alternatives studied.     

Bayesian analysis of multivariate mortality data with large families

This paper presents a Bayesian method for the analysis of toxicological multivariate mortality data when the discrete mortality rate for each family of subjects at a given time depends on familial random effects and the toxicity level experienced by the family. Our aim is to model and analyse one set of such multivariate mortality data with large family sizes: the potassium thiocyanate (KSCN) tainted fish tank data of O'Hara Hines. The model used is based on a discretized hazard with additional time-varying familial random effects. A similar previous study (using sodium thiocyanate (NaSCN)) is used to construct a prior for the parameters in the current study. A simulation-based approach is used to compute posterior estimates of the model parameters and mortality rates and several other quantities of interest. Recent tools in Bayesian model diagnostics and variable subset selection have been incorporated to verify important modelling assumptions regarding the effects of time and heterogeneity among the families on the mortality rate. Further, Bayesian methods using predictive distributions are used for comparing several plausible models.     

Graphical Techniques for Selecting Explanatory Variables for Time Series Data

Bayesian model building techniques are developed for data with a strong time series structure and possibly exogenous explanatory variables that have strong explanatory and predictive power. The emphasis is on finding whether there are any explanatory variables that might be used for modelling if the data have a strong time series structure that should also be included. We use a time series model that is linear in past observations and that can capture both stochastic and deterministic trend, seasonality and serial correlation. We propose the plotting of absolute predictive error against predictive standard deviation. A series of such plots is utilized to determine which of several nested and non-nested models is optimal in terms of minimizing the dispersion of the predictive distribution and restricting predictive outliers. We apply the techniques to modelling monthly counts of fatal road crashes in Australia where economic, consumption and weather variables are available and we find that three such variables should be included in addition to the time series filter. The approach leads to graphical techniques to determine strengths of relationships between the dependent variable and covariates and to detect model inadequacy as well as determining useful numerical summaries.     

Real-time monitoring of carbon monoxide using value-at-risk measure and control charting

One of the most important environmental health issues is air pollution, causing the deterioration of the population's quality of life, principally in cities where the urbanization level seems limitless. Among ambient pollutants, carbon monoxide (CO) is well known for its biological toxicity. Many studies report associations between exposure to CO and excess mortality. In this context, the present work provides an advanced modelling scheme for real-time monitoring of pollution data and especially of carbon monoxide pollution in city level. The real-time monitoring is based on an appropriately adjusted multivariate time series model that is used in finance and gives accurate one-step-ahead forecasts. On the output of the time series, we apply an empirical monitoring scheme that is used for the early detection of abnormal increases of CO levels. The proposed methodology is applied in the city of Athens and as the analysis revealed has a valuable performance.     

PREDICTION-FOCUSED MODEL SELECTION FOR AUTOREGRESSIVE MODELS

In order to make predictions of future values of a time series, one needs to specify a forecasting model. A popular choice is an autoregressive time‐series model, for which the order of the model is chosen by an information criterion. We propose an extension of the focused information criterion (FIC) for model‐order selection, with emphasis on a high predictive accuracy (i.e. the mean squared forecast error is low). We obtain theoretical results and illustrate by means of a simulation study and some real data examples that the FIC is a valid alternative to the Akaike information criterion (AIC) and the Bayesian information criterion (BIC) for selection of a prediction model. We also illustrate the possibility of using the FIC for purposes other than forecasting, and explore its use in an extended model.     

Revising a Prognostic Index Developed for Classification Purposes: an Application to Gastric Cancer Data

A prognostic index (PI) is usually derived from a regression model as a weighted mean of the covariates, with weights (partial scores) proportional to the parameter estimates. When a PI is applied to patients other than those considered for its development, the issue of assessing its validity on the new case series is crucial. For this purpose, Van Houwelingen (2000) proposed a method of validation by calibration, which limits overfitting by embedding the original model into a new one, so that only a few parameters will have to be estimated. Here we address the problem of PI validation and revision with the above approach when the PI has classification purposes and it represents the linear predictor of a Weibull model, derived from an accelerated failure time parameterization instead of a proportional hazards one, as originally described by Van Houwelingen. We show that the Van Houwelingen method can be applied in a straightforward manner, provided that the parameterization originally used in the PI model is appropriately taken into account. We also show that model validation and revision can be carried out by modifying the cut-off values used for prognostic grouping without affecting the partial scores of the original PI. This procedure can be applied to simplify the clinician's use of an established PI for classification purposes.