Aurora-X improves forecasting accuracy across many time series types
Aurora-X: Built for Extreme Time Series Forecasting
Machine Learning
Summary
Time series forecasting tries to predict what will happen next based on past data, like weather or stock prices. The authors introduced Aurora-X, a very large model designed to handle many different types of time series data better than before. It learns patterns step-by-step, including relationships between variables and different time lengths, and it can adapt to using longer or shorter past data when making predictions. Aurora-X also predicts probabilities of future values, providing a fuller picture of uncertainty. Tests show it usually forecasts more accurately than earlier models.
What this means in practice
- •For financial analysts: Produce more reliable multi-variable forecasts of market trends using adaptable large-scale time series models.
- •For climate modelers: Model complex environmental time series with long historical contexts and multiple influencing factors to improve weather or climate predictions.
Authors
Xingjian Wu, Chenjuan Guo, Xiangfei Qiu, Zhigang Hu, Hanyin Cheng, Peng Chen, Yang Shu, Jilin Hu, Bin Yang
Abstract
Time series foundation models (TSFMs) enable cross-domain forecasting, but their development as general-purpose forecasters remains constrained by underexplored training potential and limited architectural versatility. To address these challenges, we introduce Aurora-X, a billion-scale TSFM with a progressive curriculum and a unified architecture. We first use channel-independent pretraining to learn temporal patterns, then introduce cross-variable dependencies, varied context and horizon lengths, and future covariates if available during midtraining. Variable-resolution post-training further enables an adjustable temporal span per token at inference. With fixed model weights, this supports longer histories under a fixed token budget or fewer tokens for the same history, enabling test-time scaling. With a versatile architecture, Aurora-X supports cross-variable modeling, covariate conditioning, and parallel decoding of future patches for probabilistic forecasting. These are supported by a novel pattern-guided mixture-of-experts that expands model capacity through sparse activation and uses shallow patch similarities to constrain deep-layer routing, guiding expert specialization across heterogeneous time series. Furthermore, we propose an implicit quantile network head that predicts arbitrary quantiles to characterize predictive distributions, enhancing probabilistic forecasting flexibility. Comprehensive experiments on GIFT-Eval, TIME, FEV-Bench, TFB, and DAG-Bench demonstrate state-of-the-art forecasting performance against pretrained TSFMs and task-specific supervised models.