来自用户 张浩彬 的文献。
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21.
张浩彬
(2022-08-10 22:51):
#paper 10.1609/aaai.v34i04.6056
在现实中,具备缺失值的时序很常见。在预测中,我们往往借助缺失位置的局部信息,或者全局均值等方式对缺失值进行插补在进行预测。但是对于缺失率较高,或存在连续缺失的情况,这些方法就可能不够了。本文提出了称为Lgnet的网络结构,在基于LSTM的基础上,对于多时间序列预测问题,借助其他序列的信息,对于序列的缺失值构建基于局部和全局的插补,并且结合gan增强对全局的估计
局部特征构造:经验均值和距离该值往后最近的一点
全局特征:对整体序列进行模式的识别(模式的数量是一个超参数),然后利用局部特征作为索引,找到相似的序列模式,并进行加权构造
以数据点的局部特征作为索引
最后对缺失值的估计有,由4部分取平均:经验均值,最近值,LSTM原始网络的预测值以及全局特征。另外,本文引入gan增强对输出的预测。
最后的实验来看:(1)Lgnet能够提高预测准确率;(2)对数据缺失率进行实验,Lgnet对缺失比例有比较强的鲁棒性。
消融实验:(1)基于内存模块所构造的全局特征,对数据确实的鲁棒性有比较重要的影响;(2)加入gan,能够提高2%-10%的预测精度,尤其是对缺失较高的数据集来说,引入gan更有利于捕捉全局的数据分布
Abstract:
Multivariate time series (MTS) forecasting is widely used in various domains, such as meteorology and traffic. Due to limitations on data collection, transmission, and storage, real-world MTS data usually contains …
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Multivariate time series (MTS) forecasting is widely used in various domains, such as meteorology and traffic. Due to limitations on data collection, transmission, and storage, real-world MTS data usually contains missing values, making it infeasible to apply existing MTS forecasting models such as linear regression and recurrent neural networks. Though many efforts have been devoted to this problem, most of them solely rely on local dependencies for imputing missing values, which ignores global temporal dynamics. Local dependencies/patterns would become less useful when the missing ratio is high, or the data have consecutive missing values; while exploring global patterns can alleviate such problem. Thus, jointly modeling local and global temporal dynamics is very promising for MTS forecasting with missing values. However, work in this direction is rather limited. Therefore, we study a novel problem of MTS forecasting with missing values by jointly exploring local and global temporal dynamics. We propose a new framework øurs, which leverages memory network to explore global patterns given estimations from local perspectives. We further introduce adversarial training to enhance the modeling of global temporal distribution. Experimental results on real-world datasets show the effectiveness of øurs for MTS forecasting with missing values and its robustness under various missing ratios.
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22.
张浩彬
(2022-08-09 17:26):
#paper 10.48550/arXiv.2203.03423
Multivariate Time Series Forecasting with Latent Graph Inference 2022的文章。我觉得比较有意思的是,我感觉作者是把简单的东西套在了一个高级的框架里面(这种写作思路值得学习)文章把多变量预测问题分成了两个路线,一个是全局单变量建模(变量共享),一个是直接全局建模全局预测。而作者说他的办法是在第一个方法的基础上进行模块化扩展。具体来说,就是每个单独序列输入编码器生成隐变量。隐变量三会进入一图结构中然后得到隐变量的预测输出。再将输出解码得到最终输出。然后作者说中间的图结构,我们有两种方式,一种是全连接图网络(FC-GNN),一种是二分法图网络(BP-GNN)(我理解是GNN中聚类的一种变体,至于多少类别,则是一个超参数)。这种思路,显然效率会有很大的提升,即使是作者提到的全局GNN,因为只是对隐变量作图,效率也是有提升,更不要说通过抽样构造子图了。所以比起基线模型效率最高,完全可以理解。倒是在准确率的讨论上,实际上作者提出的网络也不全是最优的(两个数据集,一个大部分最优,另一个不是)。虽然做了个简单的消融实验,但是作者也没怎么解释。
总结下来几点:
(1)往上套一个大框架:多变量预测分成两种;embedding变成隐变量;图模型中提供了全连接+二分图的性能-效率权衡()
(2)实验不够,加模拟(这一点还真类似统计中oracle性质的讨论,貌似在深度学习的会议中相对少见)
arXiv,
2022.
DOI: 10.48550/arXiv.2203.03423
Abstract:
This paper introduces a new approach for Multivariate Time Series forecasting that jointly infers and leverages relations among time series. Its modularity allows it to be integrated with current univariate …
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This paper introduces a new approach for Multivariate Time Series forecasting that jointly infers and leverages relations among time series. Its modularity allows it to be integrated with current univariate methods. Our approach allows to trade-off accuracy and computational efficiency gradually via offering on one extreme inference of a potentially fully-connected graph or on another extreme a bipartite graph. In the potentially fully-connected case we consider all pair-wise interactions among time-series which yields the best forecasting accuracy. Conversely, the bipartite case leverages the dependency structure by inter-communicating the N time series through a small set of K auxiliary nodes that we introduce. This reduces the time and memory complexity w.r.t. previous graph inference methods from O(N^2) to O(NK) with a small trade-off in accuracy. We demonstrate the effectiveness of our model in a variety of datasets where both of its variants perform better or very competitively to previous graph inference methods in terms of forecasting accuracy and time efficiency.
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23.
张浩彬
(2022-08-09 15:58):
#paper 10.1145/3394486.3403118
Connecting the Dots_Multivariate Time Series Forecasting with Graph Neural Networks:
KDD2020的文章,感觉最大创新就是可以自适应创建图-但是细读下来又感觉乏善可陈;总的来说,感觉是把时空一套的东西套到了多时间序列中。最后也证明了,这套框架虽然可以用于多序列问题,但是还是时空效果最好,这也是不难理解、
本文提出了交MTGNN的基于图神经网络的模型来处理多序列的预测问题。本文认为多序列预测主要解决两个问题,分别是(1)怎么自适应生成图;(2)怎么在训练中根性图。
挑战1:图结构需要学习;通过3个核心组件解决:(1)图学习层:基于数据自适应提取稀疏图邻接矩阵(2)图卷积层:解决变量间的空间以来关系,转为有向图设计,避免图卷积网络的过渡平滑;(3)时间卷积层:通过一维卷积捕获时间模式--可以发现多个时间模式以及长时间网络
挑战2:训练中不仅参数需要更新,图结构也需要更新(要在端到端学习中完成这个过程):curriculum learning
回到模型的结构中:
1.大结构:
(1)图学习层计算邻接矩阵。
(2)输入数据首先通过1*1卷积核到时间卷积,再到图卷积(图卷积实际山接受三个输入:图学习层,上一层时间卷积的输出,以及时间卷积前解一个残差链接快),实际上一组操作包括了一个时间卷积核图卷积。经过n次操作。每次操作都会产生一个输出,把每次操作的输出连接在一起。时间卷积就是在序列维度处理数据,图卷积就是在图维度处理数据。
2.具体组件
(1)图学习层-通过抽样分组方法计算节点间关系,避免内存溢出-并通过一种新的度量方式确定单向距离(单向网络)
(2)图卷积层-似乎有点类似gru一样提取上层信息-成为“ 混合跳传播层”
(3)时间卷积模块-一维空洞卷积-inception--1*2 ,1*3,1*6,1*7,用这些卷积来覆盖7,12,24,28.60这样的时间周期--用扩张卷积来加大感受野使得可以提取长时间的特征
对比了几个主流结构,都取得了不错的结果。并进行了消融实验验证。消融实验验证:图卷积模块,mix-hop链接,inception,curriculum learning;并单独额外研究了图学习层。
Abstract:
Modeling multivariate time series has long been a subject that has attracted researchers from a diverse range of fields including economics, finance, and traffic. A basic assumption behind multivariate time …
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Modeling multivariate time series has long been a subject that has attracted researchers from a diverse range of fields including economics, finance, and traffic. A basic assumption behind multivariate time series forecasting is that its variables depend on one another but, upon looking closely, it is fair to say that existing methods fail to fully exploit latent spatial dependencies between pairs of variables. In recent years, meanwhile, graph neural networks (GNNs) have shown high capability in handling relational dependencies. GNNs require well-defined graph structures for information propagation which means they cannot be applied directly for multivariate time series where the dependencies are not known in advance. In this paper, we propose a general graph neural network framework designed specifically for multivariate time series data. Our approach automatically extracts the uni-directed relations among variables through a graph learning module, into which external knowledge like variable attributes can be easily integrated. A novel mix-hop propagation layer and a dilated inception layer are further proposed to capture the spatial and temporal dependencies within the time series. The graph learning, graph convolution, and temporal convolution modules are jointly learned in an end-to-end framework. Experimental results show that our proposed model outperforms the state-of-the-art baseline methods on 3 of 4 benchmark datasets and achieves on-par performance with other approaches on two traffic datasets which provide extra structural information.
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24.
张浩彬
(2022-07-30 17:14):
#paper doi:10.1287/ijoc.2021.1147,Improving Sales Forecasting Accuracy: A Tensor Factorization Approach with Demand Awareness
针对的是多个商店的多商品销售预测问题,借鉴于协同过滤思想,把数据看作高维张量,对张量进行分解,从而实现更好提取相关信息及上下文关系,并对分解后的特征接入时间序列框架SARIMA 及LSTM,实现了比传统方法更好的效果。
Abstract:
Because of the accessibility of big data collections from consumers, products, and stores, advanced sales forecasting capabilities have drawn great attention from many businesses, especially those in retail, because of …
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Because of the accessibility of big data collections from consumers, products, and stores, advanced sales forecasting capabilities have drawn great attention from many businesses, especially those in retail, because of the importance of forecasting in decision making. Improvement of forecasting accuracy, even by a small percentage, may have a substantial impact on companies’ production and financial planning, marketing strategies, inventory controls, and supply chain management. Specifically, our research goal is to forecast the sales of each product in each store in the near future. Motivated by tensor factorization methodologies for context-aware recommender systems, we propose a novel approach called the advanced temporal latent factor approach to sales forecasting, or ATLAS for short, which achieves accurate and individualized predictions for sales by building a single tensor factorization model across multiple stores and products. Our contribution is a combination of a tensor framework (to leverage information across stores and products), a new regularization function (to incorporate demand dynamics), and extrapolation of the tensor into future time periods using state-of-the-art statistical (seasonal autoregressive integrated moving-average models) and machine-learning (recurrent neural networks) models. The advantages of ATLAS are demonstrated on eight product category data sets collected by Information Resources, Inc., where we analyze a total of 165 million weekly sales transactions of over 15,560 products from more than 1,500 grocery stores. Summary of Contribution: Sales forecasting has been a task of long-standing importance. Accurate sales forecasting provides critical managerial implications for companies’ decision making and operations. Improvement of forecasting accuracy may have a substantial impact on companies’ production planning, marketing strategies, inventory controls, and supply chain management, among other things. This paper proposes a novel computational (machine-learning-based) approach to sales forecasting and thus is positioned directly at the intersection of computing and business/operations research.
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25.
张浩彬
(2022-06-25 15:38):
#paper doi:10.1007/s11356-021-17442-1,A systematic literature review of deep learning neural network for time series air quality forecasting
21年关于深度学习用于大气污染物预测的文章。算是很全面地从深度学习的角度总结了各种大气污染预测的方法,主要包括单模型、混合模型、时空网络以及结合序列分解进行深度学习预测等四个方面,并对每个方面的相关论文进行了讨论总结,相对比较详尽。美中不足的是,针对这四个方面的相互比较,作者的笔墨较少。
Environmental science and pollution research international,
2022-Jan.
DOI: 10.1007/s11356-021-17442-1
PMID: 34807385
Abstract:
Rapid progress of industrial development, urbanization and traffic has caused air quality reduction that negatively affects human health and environmental sustainability, especially among developed countries. Numerous studies on the development …
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Rapid progress of industrial development, urbanization and traffic has caused air quality reduction that negatively affects human health and environmental sustainability, especially among developed countries. Numerous studies on the development of air quality forecasting model using machine learning have been conducted to control air pollution. As such, there are significant numbers of reviews on the application of machine learning in air quality forecasting. Shallow architectures of machine learning exhibit several limitations and yield lower forecasting accuracy than deep learning architecture. Deep learning is a new technology in computational intelligence; thus, its application in air quality forecasting is still limited. This study aims to investigate the deep learning applications in time series air quality forecasting. Owing to this, literature search is conducted thoroughly from all scientific databases to avoid unnecessary clutter. This study summarizes and discusses different types of deep learning algorithms applied in air quality forecasting, including the theoretical backgrounds, hyperparameters, applications and limitations. Hybrid deep learning with data decomposition, optimization algorithm and spatiotemporal models are also presented to highlight those techniques' effectiveness in tackling the drawbacks of individual deep learning models. It is clearly stated that hybrid deep learning was able to forecast future air quality with higher accuracy than individual models. At the end of the study, some possible research directions are suggested for future model development. The main objective of this review study is to provide a comprehensive literature summary of deep learning applications in time series air quality forecasting that may benefit interested researchers for subsequent research.
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26.
张浩彬
(2022-06-20 08:27):
#paper doi:10.1145/3219819.3219822,Deep Distributed Fusion Network for Air Quality Prediction
.2018年的kdd论文,从现在的角度看,或者从当时的角度看,作者所构建的这个网络都并不复杂。这个网络主要包括2个组件:空间变换组件以及深度分布融合组件。
1.设计了一个空间变换组件,将空间稀疏的空气质量数据转换为模拟二手污染物源的一致输入, 借助来自空间邻居的信号(根据分布方位分为远近及东西南北向供16个),DeepAir 在一般情况和突变情况下具有更好的性能。
2.考虑到直接和间接因素对空气质量的影响不同,分别使用每个间接因素与直接因素进行一个子网络构建,以及构建一个整体子网络,最后进行融合。。
3.这个论文的网络结构虽然不复杂,但是却很贴近业务。是基于业务的基础上对网络进行设计的。作者基于 9 个中国城市的三年数据,结果表明 DeepAir 与 10 个基线相比具有优势。 在短期、长期和突变预测方面的相对准确度分别提高了 2.4%、12.2%、63.2%。
Abstract:
Accompanying the rapid urbanization, many developing countries are suffering from serious air pollution problem. The demand for predicting future air quality is becoming increasingly more important to government's policy-making and …
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Accompanying the rapid urbanization, many developing countries are suffering from serious air pollution problem. The demand for predicting future air quality is becoming increasingly more important to government's policy-making and people's decision making. In this paper, we predict the air quality of next 48 hours for each monitoring station, considering air quality data, meteorology data, and weather forecast data. Based on the domain knowledge about air pollution, we propose a deep neural network (DNN)-based approach (entitled DeepAir), which consists of a spatial transformation component and a deep distributed fusion network. Considering air pollutants' spatial correlations, the former component converts the spatial sparse air quality data into a consistent input to simulate the pollutant sources. The latter network adopts a neural distributed architecture to fuse heterogeneous urban data for simultaneously capturing the factors affecting air quality, e.g. meteorological conditions. We deployed DeepAir in our AirPollutionPrediction system, providing fine-grained air quality forecasts for 300+ Chinese cities every hour. The experimental results on the data from three-year nine Chinese-city demonstrate the advantages of DeepAir beyond 10 baseline methods. Comparing with the previous online approach in AirPollutionPrediction system, we have 2.4%, 12.2%, 63.2% relative accuracy improvements on short-term, long-term and sudden changes prediction, respectively.
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27.
张浩彬
(2022-05-30 19:14):
#paper Wen, Ruofeng, et al. A Multi-Horizon Quantile Recurrent Forecaster. #paper Wen, Ruofeng, et al. A Multi-Horizon Quantile Recurrent Forecaster. DOI: 10.48550/arXiv.1711.11053
MQRNN,又是亚马逊的时序论文。之前看了DeepAR,可以对多个序列进行建模,并且也有很好的鲁棒性。但是相比之前的prophet和DeepAR,MQRNN走了另外一个路子,基于分位数的预测。这样的一个好处是,它认为我们不再去预测序列在t时刻的分布,而是预测t时刻的分位数,走了分位数回归的路子。另外,相比于DeepAR,MQRNN使用了水平多无预测,即不再采用迭代方式预测多步,而是一次性产生多步预测。按照论文的说法,这样的好处是提高了预测效率(毕竟可以并行),减少了累积误差(个人觉得这点,见仁见智,本质其实一样)
arXiv,
2017.
DOI: 10.48550/arXiv.1711.11053
Abstract:
We propose a framework for general probabilistic multi-step time series regression. Specifically, we exploit the expressiveness and temporal nature of Sequence-to-Sequence Neural Networks (e.g. recurrent and convolutional structures), the nonparametric …
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We propose a framework for general probabilistic multi-step time series regression. Specifically, we exploit the expressiveness and temporal nature of Sequence-to-Sequence Neural Networks (e.g. recurrent and convolutional structures), the nonparametric nature of Quantile Regression and the efficiency of Direct Multi-Horizon Forecasting. A new training scheme, *forking-sequences*, is designed for sequential nets to boost stability and performance. We show that the approach accommodates both temporal and static covariates, learning across multiple related series, shifting seasonality, future planned event spikes and cold-starts in real life large-scale forecasting. The performance of the framework is demonstrated in an application to predict the future demand of items sold on this http URL, and in a public probabilistic forecasting competition to predict electricity price and load.
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28.
张浩彬
(2022-04-23 15:36):
#paper Salinas, D., Flunkert, V., Gasthaus, J., & Januschowski, T. (2020). DeepAR: Probabilistic forecasting with autoregressive recurrent networks. International Journal of Forecasting, 36(3), 1181–1191. https://doi.org/10.1016/j.ijforecast.2019.07.001
2020的DeepAR算法,个人觉得还是蛮有启发性的。相比于大部分在时序问题这种的点预测,DeepAR用概率模型的思路,在每个时间点去预测期概率分布;这其实也可能更符合显示,毕竟本身时序过程就是有非常强的随机属性,概率分布本身也更贴近本质。文章本身对鲁棒性讨论不多,但DeepAR的鲁棒性应该比较好。另外就是DeepAR自身强调的是,他可以很方便地对多个相关的序列(数千上万)个进行建模并提取其中的关系,这一点确实也是比较强的。所以作者也特别提到,仅需要少量的特征工程及超参数调整,即能获得比传统模型更好的效果。(论文中的模型对比,我个人觉得确实也相对规范)。
论文本身写得很精炼,但是因为是Amazon的论文,所以亲生儿子是用Mxnet上搭建的,用起来确实有点不太方便。Pytprch和TF倒是有实现,但是实现细节也有些魔改的地方。方便性来看,确实比不过Prophet,哈哈
Abstract:
Probabilistic forecasting, i.e., estimating a time series’ future probability distribution given its past, is a key enabler for optimizing business processes. In retail businesses, for example, probabilistic demand forecasts are …
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Probabilistic forecasting, i.e., estimating a time series’ future probability distribution given its past, is a key enabler for optimizing business processes. In retail businesses, for example, probabilistic demand forecasts are crucial for having the right inventory available at the right time and in the right place. This paper proposes DeepAR, a methodology for producing accurate probabilistic forecasts, based on training an autoregressive recurrent neural network model on a large number of related time series. We demonstrate how the application of deep learning techniques to forecasting can overcome many of the challenges that are faced by widely-used classical approaches to the problem. By means of extensive empirical evaluations on several real-world forecasting datasets, we show that our methodology produces more accurate forecasts than other state-of-the-art methods, while requiring minimal manual work.
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29.
张浩彬
(2022-04-19 21:09):
#paper 10.7287/peerj.preprints.3190v1 Taylor, S. J., & Letham, B. (2017, August 25). Forecasting at Scale. PeerJ Prepr 17年Facebook开源的Prophet。原理不复杂,时序分成3个部分,趋势项,周期项,节假日项。之前用在我司的一个预测模型里面,但是最近算是正儿八经的把论文给读了。Prophet被诟病最多应该还是没啥理论,尤其是趋势项的部分分解过于粗暴了,把时序上的所有点,分解的所有项都看作是t的函数,确实带有一股工业界浓浓的ML气息。虽然粗暴,但不得不说使用体验却是很好。prophet特别适用于商业时间序列的预测,并且这个包中集成了很多方便使用的工具,例如可以方便地定义节假日,方便地定于周期,中间时间序列有缺失值也不仅要,集成了异常检测识别,模型评估方法,时间序列分解图,所以说,即使不是很了解理论的人,也能够很容易使用这个包,简单而言,对数据分析师,非常友好。
PeerJ Preprints,
2017.
DOI: 10.7287/peerj.preprints.3190
Abstract:
Forecasting is a common data science task that helps organizations with capacity planning, goal setting, and anomaly detection. Despite its importance, there are serious challenges associated with producing reliable and …
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Forecasting is a common data science task that helps organizations with capacity planning, goal setting, and anomaly detection. Despite its importance, there are serious challenges associated with producing reliable and high quality forecasts — especially when there are a variety of time series and analysts with expertise in time series modeling are relatively rare. To address these challenges, we describe a practical approach to forecasting “at scale” that combines configurable models with analyst-in-the-loop performance analysis. We propose a modular regression model with interpretable parameters that can be intuitively adjusted by analysts with domain knowledge about the time series. We describe performance analyses to compare and evaluate forecasting procedures, and automatically flag forecasts for manual review and adjustment. Tools that help analysts to use their expertise most effectively enable reliable, practical forecasting of business time series.
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30.
张浩彬
(2022-03-25 11:30):
#paper 10.1360/SCM-2019-0368 气象调整下的区域空气质量评估
空气质量的有效评估是空气质量管理的重要方向。蓝天保卫战圆满收官,十四五规划中,空气改善目标依然包括了PM2.5以及空气质量的优良天数。参与过一些地方环保局在大气治理问题的一些问题处理。应该说空气质量水平一方面既受到污染物排放的影响,另一方面也受到气象因素的影响。如果能够有效摒除气象因素的干扰,那么在制定策略的时候就可能能够更加精准。回到本文,传统来说,我们固然可以通过对协变量的处理来进行单独分析。但是也会由于设置与协变量遵循了不同的基准分布有所不同也会带来偏差(毕竟气象条件是不能随机分配的)。本文提出一种在时间和空间两个维度对观测到的浓度中气象因素进行调整的新方法(非参数方法),相对于传统用的简单平均,这里结合了时间及空间因素进行调整(我觉得新,主要在于引入空间的处理。作者也证明了,带趋势分析的处理是本方法中的一个特例)。使得 调整后的均值可以在不同年份之间进行比较。作者对北京地区(覆盖范围包括扩展出去的河北)进行实证,发现SO2显著减少,PM25级NO2改善甚微,O3反而上升。(虽然是2020年论文,但数据截止到2017)。实证来看,整体应该是吻合的,臭氧水平的升高一个应该是因为北方地区相对臭氧问题不属于主要问题(这点与南方地区相反),另一点应该是毕竟蓝天多了,臭氧更容易生成。
最后感慨一下,即使是非参的方法,统计的论文(作者是陈松蹊院士和他的学生)相比cs的,大家都做模型,统计也更加关注估计和理论性质。
中国科学: 数学,
2020.
DOI: 10.1360/SCM-2019-0368
Abstract:
虽然空气污染是由污染物排放到大气中造成的,但是由于气象条件会影响污染物的扩散,因而实际观测到的污染水平会受到气象条件的影响.因此, 有效的空气质量管理要求污染评估指标和统计方法不受气象因素的干扰, 并能准确客观地反映污染物浓度的变化.为了评估北京地区潜在污染物排放的变化, 本文提出一种消除气象干扰的时空调整方法.通过控制气象条件, 调整后的污染物时空平均浓度可以捕捉到潜在排放量的变化.本文提出具体调整均值的方法, 并进行理论和数值分析,将此方法应用于北京地区的空气质量评估, 揭示一些有趣的模式和趋势, 这些结果可以用于空气质量评估和管理.
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虽然空气污染是由污染物排放到大气中造成的,但是由于气象条件会影响污染物的扩散,因而实际观测到的污染水平会受到气象条件的影响.因此, 有效的空气质量管理要求污染评估指标和统计方法不受气象因素的干扰, 并能准确客观地反映污染物浓度的变化.为了评估北京地区潜在污染物排放的变化, 本文提出一种消除气象干扰的时空调整方法.通过控制气象条件, 调整后的污染物时空平均浓度可以捕捉到潜在排放量的变化.本文提出具体调整均值的方法, 并进行理论和数值分析,将此方法应用于北京地区的空气质量评估, 揭示一些有趣的模式和趋势, 这些结果可以用于空气质量评估和管理.
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31.
张浩彬
(2022-02-27 21:33):
#paper doi:10.13546/j.cnki.tjyjc.2017.01.001 基于平衡轮换样本调查的时间序列建模
师门主要是研究抽样技术的,奈何自己关于统计抽样确实没啥储备,只能找些基础性的文章看看。本篇文章介绍了在平衡轮换样本中,如何进行实行时间序列建模。轮换样本,指的是在连续性抽样中,对每期样本进行更新轮换。这种样本抽取方式,有别于一次性抽样调查,因此如何选择合适的估计及建模方式则更加重要。本文应该说是无论是抽样方式,还是估计方法(状态空间模型+卡尔曼滤波)都算是比较经典的方法吧,最后的模型对比则用的是数值模拟的方法,这一点没太大体会。看完之后,还是感觉统计调查方法如何在当前的背景下,有新的突破,还真是不容易。
统计与决策,
2017.
DOI: 10.13546/j.cnki.tjyjc.2017.01.001
Abstract:
连续性抽样调查由于能够描述目标总体随时间的动态变化过程,吸引了越来越多国内外学者的关注。国外连续性抽样的研究已经十分成熟,在已知的轮换模式下,建立合适的模型,使得模型能较好地描述数据的真实生成过程,从而得到精度更高的目标估计量。文章建立一般轮换模式rm1rm- 12下的时间序列模型,然后以6362模式为例,利用状态空间模型和卡尔曼滤波,给出已有信息下的最优估计,有效减少抽样误差,提高样本的估计精度。
>>>
连续性抽样调查由于能够描述目标总体随时间的动态变化过程,吸引了越来越多国内外学者的关注。国外连续性抽样的研究已经十分成熟,在已知的轮换模式下,建立合适的模型,使得模型能较好地描述数据的真实生成过程,从而得到精度更高的目标估计量。文章建立一般轮换模式rm1rm- 12下的时间序列模型,然后以6362模式为例,利用状态空间模型和卡尔曼滤波,给出已有信息下的最优估计,有效减少抽样误差,提高样本的估计精度。
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