Hyun Ahn¹, Kyunghee Sun², Kwanghoon Pio Kim^3,*

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 767-779, 2022, DOI:10.32604/cmc.2022.019369 - 07 September 2021

Abstract Time series forecasting has become an important aspect of data analysis and has many real-world applications. However, undesirable missing values are often encountered, which may adversely affect many forecasting tasks. In this study, we evaluate and compare the effects of imputation methods for estimating missing values in a time series. Our approach does not include a simulation to generate pseudo-missing data, but instead perform imputation on actual missing data and measure the performance of the forecasting model created therefrom. In an experiment, therefore, several time series forecasting models are trained using different training datasets prepared More >

Fan Bingxu^*

Intelligent Automation & Soft Computing, Vol.26, No.3, pp. 549-555, 2020, DOI:10.32604/iasc.2020.013932

Abstract Poverty alleviation has always been the focus of China's work. According to the survey, the poverty population in rural areas has been reduced to a large extent, and the unemployed have had the lowest historical record in history. Big data technology is a new technology that has slowly emerged in recent years. The use of big data technology to create a visual platform for rural poverty alleviation is a relatively new idea at this stage. And we use the Map-reducebased big data missing value filling algorithm, which is designed to solve the data loss phenomenon More >