Electronics and Telecommunications Trends (전자통신동향분석)

Electronics and Telecommunications Research Institute (한국전자통신연구원)
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Technical Trends of Time-Series Data Imputation

시계열 데이터 결측치 처리 기술 동향

  • Published : 2021.08.01
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Abstract

Data imputation is a crucial issue in data analysis because quality data are highly correlated with the performance of AI models. Particularly, it is difficult to collect quality time-series data for uncertain situations (for example, electricity blackout, delays for network conditions). Thus, it is necessary to research effective methods of time-series data imputation. Many studies on time-series data imputation can be divided into 5 parts, including statistical based, matrix-based, regression-based, deep learning (RNN and GAN) based methodologies. This study reviews and organizes these methodologies. Recently, deep learning-based imputation methods are developed and show excellent performance. However, it is associated to some computational problems that make it difficult to use in real-time system. Thus, the direction of future work is to develop low computational but high-performance imputation methods for application in the real field.

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Acknowledgement

이 논문은 2021년도 정부(과학기술정보통신부)의 재원으로 정보통신산업진흥원의 지원을 받아 수행된 에너지 AI 융합 연구개발 사업임[No. S0317-21-1001]

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