Optimizing Multivariate Time Series Forecasting with Data Augmentation

Seyed Sina Aria, Seyed Hossein Iranmanesh and Hossein Hassani ()
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Seyed Sina Aria: School of Industrial Engineering, College of Engineering, University of Tehran, Tehran 19395-4697, Iran
Seyed Hossein Iranmanesh: School of Industrial Engineering, College of Engineering, University of Tehran, Tehran 19395-4697, Iran
Hossein Hassani: School of Industrial Engineering, College of Engineering, University of Tehran, Tehran 19395-4697, Iran

JRFM, 2024, vol. 17, issue 11, 1-19

Abstract: The convergence of data mining and deep learning has become an invaluable tool for gaining insights into evolving events and trends. However, a persistent challenge in utilizing these techniques for forecasting lies in the limited access to comprehensive, error-free data. This challenge is particularly pronounced in financial time series datasets, which are known for their volatility. To address this issue, a novel approach to data augmentation has been introduced, specifically tailored for financial time series forecasting. This approach leverages the power of Generative Adversarial Networks to generate synthetic data that replicate the distribution of authentic data. By integrating synthetic data with real data, the proposed approach significantly improves forecasting accuracy. Tests with real datasets have proven that this method offers a marked improvement over models that rely only on real data.

Keywords: forecasting; multivariate time series; data augmentation; deep learning; financial time series; Wasserstein Generative Adversarial Network; Bidirectional Long Short-Term Memory; data mining (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2024
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