Loss-Driven Adversarial Ensemble Deep Learning for On-Line Time Series Analysis

Hyungjin Ko, Jaewook Lee, Junyoung Byun, Bumho Son and Saerom Park
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Hyungjin Ko: Industrial Engineering, Seoul National University, Seoul 08826, Korea
Jaewook Lee: Industrial Engineering, Seoul National University, Seoul 08826, Korea
Junyoung Byun: Industrial Engineering, Seoul National University, Seoul 08826, Korea
Bumho Son: Industrial Engineering, Seoul National University, Seoul 08826, Korea
Saerom Park: Industrial Engineering, Seoul National University, Seoul 08826, Korea

Sustainability, 2019, vol. 11, issue 12, 1-24

Abstract: Developing a robust and sustainable system is an important problem in which deep learning models are used in real-world applications. Ensemble methods combine diverse models to improve performance and achieve robustness. The analysis of time series data requires dealing with continuously incoming instances; however, most ensemble models suffer when adapting to a change in data distribution. Therefore, we propose an on-line ensemble deep learning algorithm that aggregates deep learning models and adjusts the ensemble weight based on loss value in this study. We theoretically demonstrate that the ensemble weight converges to the limiting distribution, and, thus, minimizes the average total loss from a new regret measure based on adversarial assumption. We also present an overall framework that can be applied to analyze time series. In the experiments, we focused on the on-line phase, in which the ensemble models predict the binary class for the simulated data and the financial and non-financial real data. The proposed method outperformed other ensemble approaches. Moreover, our method was not only robust to the intentional attacks but also sustainable in data distribution changes. In the future, our algorithm can be extended to regression and multiclass classification problems.

Keywords: ensemble deep learning; on-line learning; time series analysis; adaptive learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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