Multivariate Time-Series Imagification with Time Embedding in Constrained Environments (Student Abstract)
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.
Cite
Text
Kang and Jo. "Multivariate Time-Series Imagification with Time Embedding in Constrained Environments (Student Abstract)." AAAI Conference on Artificial Intelligence, 2024. doi:10.1609/AAAI.V38I21.30461Markdown
[Kang and Jo. "Multivariate Time-Series Imagification with Time Embedding in Constrained Environments (Student Abstract)." AAAI Conference on Artificial Intelligence, 2024.](https://mlanthology.org/aaai/2024/kang2024aaai-multivariate/) doi:10.1609/AAAI.V38I21.30461BibTeX
@inproceedings{kang2024aaai-multivariate,
title = {{Multivariate Time-Series Imagification with Time Embedding in Constrained Environments (Student Abstract)}},
author = {Kang, Seungwoo and Jo, Ohyun},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2024},
pages = {23535-23536},
doi = {10.1609/AAAI.V38I21.30461},
url = {https://mlanthology.org/aaai/2024/kang2024aaai-multivariate/}
}