@inproceedings{
lee2024hypernetwork,
title={HyperNetwork Approximating Future Parameters for Time Series Forecasting under Temporal Drifts},
author={Jaehoon Lee and Chan Kim and Gyumin Lee and Haksoo Lim and Jeongwhan Choi and Kookjin Lee and Dongeun Lee and Sanghyun Hong and Noseong Park},
booktitle={NeurIPS 2023 Workshop on Distribution Shifts: New Frontiers with Foundation Models},
year={2024},
url={https://openreview.net/forum?id=7yVBYSPI8Z}
}
Models for time series forecasting require the ability to extrapolate from previous observations. Yet, extrapolation is challenging, especially when the data spanning several periods is under temporal drifts where each period has a different distribution. To address this problem, we propose HyperGPA, a hypernetwork that generates a target model's parameters that are expected to work well (i.e., be an optimal model) for each period. HyperGPA discovers an underlying hidden dynamics which causes temporal drifts over time, and generates the model parameters for a target period, aided by the structures of computational graphs. In comprehensive evaluations, we show that target models whose parameters are generated by HyperGPA are up to 64.1\% more accurate than baselines.
HyperNetwork Approximating Future Parameters for Time Series Forecasting under Temporal Drifts
Jaehoon Lee Chan Kim Gyumin Lee Haksoo Lim Jeongwhan Choi Kookjin Lee Dongeun Lee Sanghyun Hong Noseong Park