Multivariate Time-Series Imagification with Time Embedding in Constrained Environments (Student Abstract)

Authors

  • Seung Woo Kang Chungbuk National University, Department of Computer Science, 28644, Cheongju, Republic of Korea
  • Ohyun Jo Chungbuk National University, Department of Computer Science, 28644, Cheongju, Republic of Korea

DOI:

https://doi.org/10.1609/aaai.v38i21.30461

Keywords:

Imagification, Convolutional Neural Networks, Time Embedding, Re-stacking

Abstract

We present an imagification approach for multivariate time-series data tailored to constrained NN-based forecasting model training environments. Our imagification process consists of two key steps: Re-stacking and time embedding. In the Re-stacking stage, time-series data are arranged based on high correlation, forming the first image channel using a sliding window technique. The time embedding stage adds two additional image channels by incorporating real-time information. We evaluate our method by comparing it with three benchmark imagification techniques using a simple CNN-based model. Additionally, we conduct a comparison with LSTM, a conventional time-series forecasting model. Experimental results demonstrate that our proposed approach achieves three times faster model training termination while maintaining forecasting accuracy.

Published

2024-03-24

How to Cite

Kang, S. W., & Jo, O. (2024). Multivariate Time-Series Imagification with Time Embedding in Constrained Environments (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 38(21), 23535-23536. https://doi.org/10.1609/aaai.v38i21.30461