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A Generic Approach of Filling Missing Values in NCDC Weather Stations Data

机译：填补NCDC气象站数据中缺失值的通用方法

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摘要

Missing data is a common problem in several real applications. Moreover, mainstream solutions to solve the missing data issue either fill in the missing values (imputation) that aim to complete dataset or ignore the missing data (marginalization) but these solutions may have notable costs in the final decision. Imputed values are considered as the same as the actually observed data where the validation of it based on the method used to predict it. In general, machine learning algorithms cannot analyze weather dataset that has missing values. Grouping stations are used to identify the group of similar stations based on the number of missing data in weather datasets, such as NCDC. In contrast, this work presents a new framework for filling missing values in the observed features based on the group of the station, which identifies by the total number of missing data of each station. The proposed method presents a simple way for grouping stations based on the missing data, and the type of group outputted by grouping process is identifying the technique that is used to fill missing values of each station. In experiments on NCDC data, we show that the new system is an effective way to enable imputation of missing values.
机译：在一些实际应用中，数据丢失是一个普遍的问题。此外，用于解决丢失数据问题的主流解决方案要么填写旨在完成数据集的丢失值（计算），要么忽略丢失数据（边际化），但这些解决方案在最终决策中可能会产生可观的成本。推定值被认为与实际观察到的数据相同，在此情况下，推定值基于用于预测值的方法进行了验证。通常，机器学习算法无法分析缺少值的天气数据集。分组站用于根据天气数据集（如NCDC）中丢失数据的数量来识别相似站的组。相反，这项工作提出了一个新的框架，用于基于站点组填充观测到的特征中的缺失值，该框架通过每个站点的缺失数据总数进行标识。所提出的方法提出了一种基于丢失数据对站点进行分组的简单方法，并且通过分组过程输出的分组的类型是识别用于填充每个站点的缺失值的技术。在对NCDC数据的实验中，我们表明，新系统是启用估算缺失值的有效方法。

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