[1] Halawi, Danny, et al. "Approaching Human-Level Forecasting with Language Models." Advances in Neural Information Processing Systems 36 (2024).

[3] Liu, Haoxin, et al. "Time-MMD: Multi-Domain Multimodal Dataset for Time Series Analysis." Advances in Neural Information Processing Systems 36 (2024).

[4] Lee, Seunghan, Kibok Lee, and Taeyoung Park. "ANT: Adaptive Noise Schedule for Time Series Diffusion Models." Advances in Neural Information Processing Systems 36 (2024).

[5] Liu, Jingwei, et al. "Retrieval-Augmented Diffusion Models for Time Series Forecasting." Advances in Neural Information Processing Systems 36 (2024).

[6] Tan, Mingtian, et al. "Are language models actually useful for time series forecasting?." Advances in Neural Information Processing Systems 36 (2024).

[7] Kim, Dongbin, et al. "Are Self-Attentions Effective for Time Series Forecasting?." Advances in Neural Information Processing Systems 36 (2024).

[8] Seo, Jun, et al. "Adaptive Information Routing for Multi Modal Time Series Forecasting." NeurIPS Workshop on Time Series in the Age of Large Models.

[9] Cao, Defu, et al. "Tempo: Prompt-based generative pre-trained transformer for time series forecasting." International Conference on Learning Representations 13 (2024).

[10] Liu, Xu, et al. "Unitime: A language-empowered unified model for cross-domain time series forecasting." Proceedings of the ACM on Web Conference 2024.

[11] Jin, Ming, et al. "Time-llm: Time series forecasting by reprogramming large language models." International Conference on Learning Representations 13 (2024).

[12] Chen, Si-An, et al. "TSMixer: An All-MLP Architecture for Time Series Forecasting." Transactions on Machine Learning Research.