@inproceedings{
bao2024boundaryaware,
title={Boundary-Aware Periodicity-based Sparsification Strategy for Ultra-Long Time Series Forecasting},
author={Yiying Bao and Hao Zhou and Chao Peng and Chenyang Xu and Shuo Shi and Kecheng Cai},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=1D984UTCxU}
}
In various domains such as transportation, resource management, and weather forecasting, there is an urgent need for methods that can provide predictions over a sufficiently long time horizon to encompass the period required for decision-making and implementation. Compared to traditional time series forecasting, ultra-long time series forecasting requires enhancing the model’s ability to infer long-term series, while maintaining inference costs within an acceptable range. To address this challenge, we propose the Boundary-Aware Periodicity-based sparsification strategy for Ultra-Long time series forecasting (BAP-UL). The periodicity-based sparsification strategy is a general lightweight data sparsification framework that captures periodic features in time series and reorganizes inputs and outputs into shorter sub-sequences for model prediction. The boundary-aware method, combined with the bounded nature of time series, improves the model’s capability to predict extreme peaks and irregular time series by adjusting the prediction results. We conducted extensive experiments on benchmark datasets, and the BAP-UL model achieved nearly 90% state-of-the-art results under various experimental conditions. Moreover, the data sparsification method based on the periodicity, proposed in this paper, exhibits broad applicability. It enhances the upper limit of sequence length for mainstream time series forecasting models and achieves the state-of-the-art prediction results.
Boundary-Aware Periodicity-based Sparsification Strategy for Ultra-Long Time Series Forecasting
Yiying Bao Hao Zhou Chao Peng Chenyang Xu Shuo Shi Kecheng Cai