일변량 시계열의 다중 특징 추출을 통한 시계열 예측 모델 설명.

As machine learning technologies are recently applied to various industries, the importance of XAI (eXplainable Artificial Intelligence) is being emphasized to understand and effectively manage the behavior of developed AI models. In time series models, XAI issues are also critical, however if the number of features is not sufficient, it is not easy to apply XAI techniques. To this end, this paper presents a multi-feature extraction method to ensure an explainable model from univariate time series. First, a multivariate time series is extracted from a univariate time series using a time series decomposition and feature engineering library. Next, a prediction model is generated from the multivariate time series using a machine learning algorithm. Finally, the explanation results for the model created using the XAI technique are represented. In this regard, the proposed method is verified through an experiment using load data of distribution lines of Korea Electric Power Corporation. [ABSTRACT FROM AUTHOR]