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Improving deep learning models Considering the time lags between Explanatory and Response Variables

JOURNAL OF INFORMATION PROCESSING SYSTEMS 2024年第3期20卷 345-359页

作者： Kim, Chaehyeon Lee, Ki Yong Sookmyung Womens Univ Dept Comp Sci Seoul South Korea Univ Penn Dept Comp & Informat Sci Philadelphia PA USA

A regression model represents the relationship between explanatory and response variables. In real life, explanatory variables often affect a response variable with a certain time lag, rather than immediately. For example, the marriage rate affects the birth rate with a time lag of 1 to 2 years. Although deep learning models have been successfully used to model various relationships, most of them do not consider the time lags between explanatory and response variables. Therefore, in this paper, we propose an extension of deep learning models, which automatically finds the time lags between explanatory and response variables. The proposed method finds out which of the past values of the explanatory variables minimize the error of the model, and uses the found values to determine the time lag between each explanatory variable and response variables. After determining the time lags between explanatory and response variables, the proposed method trains the deep learning model again by reflecting these time lags. Through various experiments applying the proposed method to a few deep learning models, we confirm that the proposed method can find a more accurate model whose error is reduced by more than 60% compared to the original model.

关键词： deep learning model optimization regression model time lag

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