A Deep Learning Approach for Time Series Data Correction: Integrating Autoencoder-Based GANs and Correlation Analysis
Year: 2026
Type: article
Abstract: This paper presents a novel correction framework for multivariate time series data that enhances data quality by integrating Autoencoder-based Generative Adversarial Networks (GANs) with correlation-aware analysis. With the widespread adoption of deep learning models for sensor-driven applications such as quality control and demand forecasting, data anomalies caused by sensor faults and network errors have emerged as a critical challenge, often degrading model performance. In multivariate time series, anomaly correction is particularly difficult because erroneous values must be distinguished from legitimate temporal variations while preserving inter-variable dependencies and temporal consistency. To address this challenge, we propose a data correction method that combines deep learning–based anomaly detection with correlation-driven correction. An Autoencoder-based GAN is employed to identify anomalous patterns in multivariate time series, while a window relevance matrix is introduced to guide precise correction. This matrix captures complex relationships among variables by jointly incorporating dynamic time warping and Pearson correlation coefficients within sliding windows. By leveraging both temporal alignment and statistical dependency, the proposed approach performs anomaly correction that maintains structural coherence across variables. Extensive experiments conducted on diverse multivariate time series datasets demonstrate that the corrected data consistently improves predictive performance compared to raw data. These results indicate that the proposed method effectively enhances data quality and model reliability, offering a robust solution for anomaly correction in multivariate time series applications.
DOI: 10.14419/ezf67m88
Authors: Sohyeon YUN, Han-joon Kim
Institution: University of Seoul
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7155706015
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