EXAONE Demand 1.0: A Time Series Foundation Model for Demand Forecasting

Title:EXAONE Demand 1.0: A Time Series Foundation Model for Demand Forecasting

Abstract:Time series foundation models (TSFMs) are pretrained on series from diverse domains, where demand series make up only a small fraction. Demand data has properties that such corpora rarely contain: Short histories, frequent zeros, censoring by stock-outs, and exogenous events that the series does not record. To this end, we propose EXAONE Demand, built on 1) a demand-specific corpus and 2) a demand-aware adapter. For the corpus, we assemble 11.3M series and 48.4B observations from 73 sources, and a synthetic generator supplies the behaviour that open demand data under-represents. For the adapter, we attach low-rank branches to a frozen general-domain backbone, one for each of the four demand classes (smooth, intermittent, erratic, and lumpy), and a router that reads eight scale-free statistics of the input series decides how much each branch contributes. We build EXAONE Demand in two versions, one trained on real-world and synthetic demand together and one trained on the synthetic corpus alone. On 22 held-out datasets, both versions outperform 36 TSFMs, and real-world demand adds a gain over synthetic data alone.

Technical report of EXAONE Demand 1.0

Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

arXiv:2609.30880 [cs.AI]

(or arXiv:2609.30880v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.30880 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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