基于SARIMA-SVR组合模型的 空气质量指数预测

空气质量指数(AQI)在波动中既具有整体的时间序列线性特征和明显的季节性波动周期,又具有多种因素影响的不确定性,为了提高AQI的预测精度,基于Ri386 3.3.3和Matlab R2014a两种编程软件,提出了一种同时具有线性和非线性的复合特征的时间序列预测模型——SARIMA-SVR组合模型。以太原市2014年1月—2019年7月的AQI月均值数据为基础,利用SARIMA时间序列模型进行线性预测,利用SVR模型对残差进行非线性预测,加和得到组合预测模型的预测结果,分析比较SARIMA,SVR和SARIMA-SVR这3种模型的预测结果和平均绝对百分比误差。结果表明,组合预测模型发挥了2种模型各自的优势,相较于单一预测模型的预测结果而言,其预测精度更高,稳定性更好。通过此模型得到的空气质量预测结果不仅可为人们的日常生活提供指导,而且可为大气污染的防治工作提供科学依据和借鉴意义。     

A hybrid information approach to predict corporate credit risk

This study proposes a hybrid information approach to predict corporate credit risk. In contrast to the previous literature that debates which credit risk model is the best, we pool information from a diverse set of structural and reduced‐form models to produce a model combination based on credit risk prediction. Compared with each single model, the pooled strategies yield consistently lower average risk prediction errors over time. We also find that while the reduced‐form models contribute more in the pooled strategies for speculative‐grade names and longer maturities, the structural models have higher weights for shorter maturities and investment grade names.     

基于限制因素组合序列的耕地质量提升潜力研究*——以什邡市为例

[目的]通过分析什邡市耕地质量现状,充分把握影响耕地质量的主导因素并进行质量提升分区,测算不同质量提升区的提升潜力,以期为全市耕地质量的建设与发展指明方向。[方法]依据农用地分等理论,构建2014年什邡市耕地质量限制性指数评价体系,采用限制性指数模型测算耕地质量限制性指数,结合因素组合法、耕地质量潜力测算法,分别进行耕地质量提升分区及其提升潜力评价。[结果]什邡市耕地质量现状差异显著,地域分布特征明显;因素限制程度以中限制为主,多因素共同主导限制耕地比重大;各质量分区限制特征明显,各区耕地质量提升潜力在时序上和空间上也存在差异。高等别耕地集中分布于南部平原,受土壤酸碱度中等限制和剖面构型高程度限制为主;北部丘陵区以及南部河谷低洼地带主要为低等别耕地,表现为坡度与灌溉因素较高程度限制为主;综合提升潜力较大的耕地集中于北部丘陵区与南部河谷低洼地带。[结论]什邡市范围内耕地质量差异明显,存在普遍限制且分区限制特征明显,各区提升潜力空间差异明显。     

重大疫情对应急物流系统建设的启示

新型冠状病毒引发重大疫情,暴露出众多的物流问题,针对“急”和“乱”这两个应急物流的主要特点,从业务、管理(执行)、技术三个层面提出建设应急物流系统的新思想,即采用平战结合、军民融合的建设方法,重点建设全国统一的应急响应物流信息平台,运用大数据、AI等技术手段,快速完成物流需求分析,充分应用自动化、智能化和无人机、机器人等先进技术手段,培养一支技术全面的国家队伍,完成应急物流的快速响应。     

Correction to: Optimal and robust combination of forecasts via constrained optimization and shrinkage

We provide a correction to Proposition 1 in Optimal and robust combination of forecasts via constrained optimization and shrinkage, published in the International Journal of Forecasting 38(1):97-116 (2021). This correction has no impact on any other result (neither theoretical nor empirical) provided in the above paper.     

Mixed-frequency approaches to nowcasting GDP: An application to Japan

In this paper, we discuss the approaches to nowcasting Japan’s GDP quarterly growth rates, comparing a variety of mixed frequency approaches including a bridge equation approach, Mixed-Data Sampling (MIDAS) and factor-augmented version of these approaches. In doing so, we examine the usefulness of a novel sparse principal component analysis (SPCA) approach in extracting factors from the dataset. We also discuss the usefulness of forecast combination, considering various ways to combine forecasts from models and surveys. Our findings are summarized as follows. First, some of the mixed frequency models discussed in this paper record out-of-sample performance superior to a naïve constant growth model. Second, albeit small, the SPCA approach of extracting factors improves predictive power compared with traditional principal component approach. Furthermore, we find that there is a gain from combining model forecasts and professional survey forecasts.     

Adoption of precision farming tools: A context-related analysis

This paper deals with precision farming tools (PFTs), a way of farming which relies on specialized equipment, software and information technologies services, whose importance is underlined in recent documents of the European Union. Precision farming is an integrated and sustainable farm management system making use of modern technologies to increase farm’s profitability, by reducing environmental impact. In this paper we explore the complex mechanisms that affect PFT’s adoption by Italian farmers. More precisely, we try to analyse the context-related factors affecting adoption of PFTs in the Italian farms.Little research has been carried out in Italy on this topic, therefore our paper tries to fill a gap in literature. In order to investigate the process of technology adoption related to precision agriculture, a questionnaire was submitted to a sample of Italian farms. The questionnaire has been structured in order to apply the AKAP (Awareness, Knowledge, Adoption, Product) sequence. Our analysis underlines that context-related factors are fundamental dimensions to be explored in order to specify uptake of PFTs. Therefore, the paper has relevant policy implications, within the context of a new participatory approach to agricultural innovation characterized by bottom-up processes boosted by farmers, which has informed the recent policies of agricultural innovation at the EU level.     

Combining prediction intervals in the M4 competition

The 2018 M4 Forecasting Competition was the first M Competition to elicit prediction intervals in addition to point estimates. We take a closer look at the twenty valid interval submissions by examining the calibration and accuracy of the prediction intervals and evaluating their performances over different time horizons. Overall, the submissions fail to estimate the uncertainty properly. Importantly, we investigate the benefits of interval combination using six recently-proposed heuristics that can be applied prior to learning about the realizations of the quantities. Our results suggest that interval aggregation offers improvements in terms of both calibration and accuracy. While averaging interval endpoints maintains its practical appeal as being simple to implement and performs quite well when data sets are large, the median and the interior trimmed average are found to be robust aggregators for the prediction interval submissions across all 100,000 time series.     

构建高校大学生党员教育新模式探索

为进一步提升高校党建实效,高校大学生党员教育工作亟待创新型机制,笔者以高校志愿工作为突破口,通过分析志愿工作之现状,探索其和学生党员教育工作的联动机制,旨在为当前学生党员教育工作注入新的生机和活力。