## 5 Conclusions  
Our proposed method reduces the level of data imbalance and improves analytic accuracy by generating outliers based on time information in consideration of the periodicity and variability of the body weight lifelog. Through the method, the problem of body weight outlier class imbalance is solved, and the time-dependent characteristics of daily variability and periodicity of weight data can be reflected. Specifically, outliers are generated through the search module and generation module, which can solve the problem of weight outlier class imbalance and reflect the time-dependent characteristics of small daily variability and periodicity of weight data: (1) For body weight data with small daily variability and periodicity, processing class imbalance by integrating time information would be more effective than data augmentation by learning all data in general. (2) It can be effective to detect outliers by generating outliers and then providing data to a machine learning algorithm to detect the outliers, which is an effective data preprocessing method for detecting actual weight anomalies. (3) Even an outlier that does not differ significantly from the normal value can be effectively detected through the relevant preprocessing method.  
Through the experiments of this study, it was confirmed that the proposed outlier generation method improved the performance of machine learning anomaly detection the most. This suggests that a preprocessing method for generating outliers specialized for body weight data is important, and since performance is greatly improved compared to no preprocessing, it suggests that a preprocessing methodology suitable for data characteristics is important and would improve performance. A study on skin lesion images data that improved deep learning performance using an augmentation methodology [49], a study that applied and compared various preprocessing methodologies specific to univariate volatile time series data [50], and a comparative study of preprocessing on medical data [51] are all consistent with the conclusion of this study that it is important to preprocess according to the characteristics of the data and can improve performance. Therefore, preprocessing suitable for the characteristics of the data is important.  
We have a plan to apply our proposed method with not only lifelogs but also various industries in the future. It is expected to be applicable to a data set that is not volatile and occurs periodically. For example, we plan to further verify that it can be applied in financial industries such as stocks, or in processes where there is a fine adjustment of values. In addition, optimization research on the noise in each variable will be conducted together.  
Conceptualization, J.K. and M.P.; data curation, J.K.; formal analysis, J.K.; funding acquisition, M.P.; methodology, J.K. and M.P.; supervision, M.P.; validation, J.K.; visualization, J.K.; writing--original draft preparation, J.K. and M.P.; writing--review and editing, M.P. All authors have read and agreed to the published version of the manuscript. This work was supported by a research grant from Seoul Women's University (2021-0423). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Data Availability Statement: Not applicable. Conflicts of Interest: The authors declare no conflict of interest.