ChronosX: Adapting Pretrained Time Series Models with Exogenous Variables

• URL: http://arxiv.org/abs/2503.12107v1
• Date: Sat, 15 Mar 2025 12:34:19 GMT
• Title: ChronosX: Adapting Pretrained Time Series Models with Exogenous Variables
• Authors: Sebastian Pineda Arango, Pedro Mercado, Shubham Kapoor, Abdul Fatir Ansari, Lorenzo Stella, Huibin Shen, Hugo Senetaire, Caner Turkmen, Oleksandr Shchur, Danielle C. Maddix, Michael Bohlke-Schneider, Yuyang Wang, Syama Sundar Rangapuram,
• Abstract summary: This paper introduces a new method to incorporate covariates into pretrained time series forecasting models.<n>Our proposed approach incorporates covariate information into pretrained forecasting models through modular blocks.<n>In evaluations on both synthetic and real datasets, our approach effectively incorporates covariate information into pretrained models, outperforming existing baselines.
• Score: 30.679739751673655
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Covariates provide valuable information on external factors that influence time series and are critical in many real-world time series forecasting tasks. For example, in retail, covariates may indicate promotions or peak dates such as holiday seasons that heavily influence demand forecasts. Recent advances in pretraining large language model architectures for time series forecasting have led to highly accurate forecasters. However, the majority of these models do not readily use covariates as they are often specific to a certain task or domain. This paper introduces a new method to incorporate covariates into pretrained time series forecasting models. Our proposed approach incorporates covariate information into pretrained forecasting models through modular blocks that inject past and future covariate information, without necessarily modifying the pretrained model in consideration. In order to evaluate our approach, we introduce a benchmark composed of 32 different synthetic datasets with varying dynamics to evaluate the effectivity of forecasting models with covariates. Extensive evaluations on both synthetic and real datasets show that our approach effectively incorporates covariate information into pretrained models, outperforming existing baselines.

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