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

arXiv:2609.30880 · cs.AI, cs.LG · Submitted 2026-09-25 · Read on arXiv

cs.AI, cs.LG

Submitted: 2026-09-25

Updated: 2026-09-25

Code: https://github.com/LGAI-Research/EXAONE-Forecast

Terminology

• Foundation Model — 16× in this paper · explained in 3 episodes
• Benchmark — 15× in this paper · explained in 2 episodes
• Time Series Forecasting — 14× in this paper · explained in 3 episodes
• Foundation Models — 10× in this paper · explained in 7 episodes
• Transformer — 9× in this paper · explained in 2 episodes
• Accuracy — 4× in this paper · explained in 1 episode
• Verification — 3× in this paper · explained in 2 episodes
• Mixture of Experts — 2× in this paper · explained in 2 episodes
• Pairwise Comparisons — 2× in this paper · explained in 2 episodes
• Provenance — 2× in this paper · explained in 4 episodes
• Time Series Foundation Models (TSFMs) — 2× in this paper · explained in 2 episodes
• Trace — 2× in this paper · explained in 2 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Frozen model — 1× in this paper · explained in 3 episodes
• In-Context Learning — 1× in this paper · explained in 4 episodes
• Instruction Tuning — 1× in this paper · explained in 2 episodes
• Probabilistic Forecasting — 1× in this paper · explained in 3 episodes
• World Model — 1× in this paper · explained in 7 episodes

Sources

• Chronos-2: From Univariate to Universal Forecasting
• An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
• Toto: Time Series Optimized Transformer for Observability
• Reverso: Efficient Time Series Foundation Models for Zero-shot Forecasting
• From Tables to Time: Extending TabPFN-v2 to Time Series Forecasting
• Beyond Magnitude and Shape: A Direction-Aware Loss for Time Series Forecasting
• EXAONE Finance 1.0: An Attention-free Time Series Foundation Model for Financial Time Series
• TempoPFN: Synthetic Pre-training of Linear RNNs for Zero-shot Time Series Forecasting
• Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting
• fev-bench: A Realistic Benchmark for Time Series Forecasting
• Output Scaling: YingLong-Delayed Chain of Thought in a Large Pretrained Time Series Forecasting Model

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