Long-term Time Series Forecasting based on Decomposition and Neural Ordinary Differential Equations

• URL: http://arxiv.org/abs/2311.04522v2
• Date: Fri, 10 Nov 2023 08:45:16 GMT
• Title: Long-term Time Series Forecasting based on Decomposition and Neural Ordinary Differential Equations
• Authors: Seonkyu Lim, Jaehyeon Park, Seojin Kim, Hyowon Wi, Haksoo Lim, Jinsung Jeon, Jeongwhan Choi, Noseong Park
• Abstract summary: Long-term time series forecasting is a challenging task that has been investigated in various domains such as finance investment, health care, traffic, and weather forecasting. We propose LTSF-DNODE, which applies a model based on linear ordinary differential equations (ODEs) and a time series decomposition method according to data statistical characteristics.
• Score: 23.51377034692954
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Long-term time series forecasting (LTSF) is a challenging task that has been investigated in various domains such as finance investment, health care, traffic, and weather forecasting. In recent years, Linear-based LTSF models showed better performance, pointing out the problem of Transformer-based approaches causing temporal information loss. However, Linear-based approach has also limitations that the model is too simple to comprehensively exploit the characteristics of the dataset. To solve these limitations, we propose LTSF-DNODE, which applies a model based on linear ordinary differential equations (ODEs) and a time series decomposition method according to data statistical characteristics. We show that LTSF-DNODE outperforms the baselines on various real-world datasets. In addition, for each dataset, we explore the impacts of regularization in the neural ordinary differential equation (NODE) framework.

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