NeurIPS 2025

Time-o1: Time-Series Forecasting Needs
Transformed Label Alignment
Hao Wang1∗
Licheng Pan1∗
Zhichao Chen2
Xu Chen3†
Qingyang Dai4
Lei Wang3
Haoxuan Li5†
Zhouchen Lin2,6,7†
1Xiaohongshu Inc.
2State Key Lab of General AI, School of Intelligence Science and Technology, Peking University
3Gaoling School of Artificial Intelligence, Renmin University of China
4Department of Control Science and Engineering, Zhejiang University
5Center for Data Science, Peking University
6Institute for Artificial Intelligence, Peking University
7Pazhou Laboratory (Huangpu), Guangzhou, Guangdong, China
Abstract
Training time-series forecast models presents unique challenges in designing ef-
fective learning objectives. Existing methods predominantly utilize the temporal
mean squared error, which faces two critical challenges: (1) label autocorrelation,
which leads to bias from the label sequence likelihood; (2) excessive amount of
tasks, which increases with the forecast horizon and complicates optimization.
To address these challenges, we propose Time-o1, a transformation-augmented
learning objective for training time-series forecasting models. The central idea is
to transform the label sequence into decorrelated components with discriminated
significance. Models are then trained to align the most significant components,
thereby effectively mitigating label autocorrelation and reducing task amount.
Extensive experiments demonstrate that Time-o1 achieves state-of-the-art per-
formance and is compatible with various forecast models. Code is available at
https://github.com/Master-PLC/Time-o1.
1
Introduction
Time-series forecasting involves predicting future data from historical observations [60, 23] and
has been applied across diverse domains, such as air quality prediction in meteorology [28], user
behavior analysis in e-commerce [3], and process monitoring in manufacturing [47, 50]. To build
effective forecast models, there are two questions that warrant investigation: (1) How to design a
ne...