| Abstract |
Weather data collected from automated weather stations have become a crucial component for making decisions in agriculture and in forestry. Over time, weather stations may become out-of-order or stopped for maintenance, and therefore, during those periods, the data values will be missing. Unfortunately, this will cause huge problems when analysing the data. The main aim of this study is to create high-quality historical weather datasets by dealing efficiently with missing values. In this paper, we present a set of missing data imputation methods and study their effectiveness. These methods were used based on different types of missing values. The experimental results show that two the proposed methods are very promising and can be used at larger scale. © 2019 Association for Computing Machinery. ACM |
