Arjun Ashok

Publications

Beyond Naïve Prompting: Strategies for Improved Zero-shot Context-aided Forecasting with LLMs. Arjun Ashok, Andrew Williams, Vincent Zhihao Zheng, Irina Rish, Nicolas Chapados, Étienne Marcotte, Valentina Zantedeschi, Alexandre Drouin. At Workshop at the Neural Information Processing Systems (NeurIPS), 2025.

DualChronos: Context-Aided Time Series Forecasting with Dual Modalities. Vincent Zhihao Zheng, Arjun Ashok, Andrew Williams, Valentina Zantedeschi, Nicolas Chapados, Alexandre Drouin, Étienne Marcotte. At NOW AI, 2025.

Beyond Naïve Prompting: Strategies for Improved Zero-shot Context-aided Forecasting with LLMs. Arjun Ashok, Andrew Williams, Vincent Zhihao Zheng, Irina Rish, Nicolas Chapados, Étienne Marcotte, Valentina Zantedeschi, Alexandre Drouin. At Conference on Language Modeling (COLM), 2025.

Context is Key: A Benchmark for Forecasting with Essential Textual Information. Andrew Williams, Arjun Ashok, Étienne Marcotte, Valentina Zantedeschi, Jithendaraa Subramanian, Roland Riachi, James Requeima, Alexandre Lacoste, Irina Rish, Nicolas Chapados, Alexandre Drouin. At International Conference on Machine Learning (ICML), 2025.

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Context is Key: A Benchmark for Forecasting with Essential Textual Information. Andrew Williams, Arjun Ashok, Étienne Marcotte, Valentina Zantedeschi, Jithendaraa Subramanian, Roland Riachi, James Requeima, Alexandre Lacoste, Irina Rish, Nicolas Chapados, Alexandre Drouin. At Workshop at the Neural Information Processing Systems (NeurIPS), 2024.

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Context is Key: A Benchmark for Forecasting with Essential Textual Information. Andrew Williams, Arjun Ashok, Étienne Marcotte, Valentina Zantedeschi, Jithendaraa Subramanian, Roland Riachi, James Requeima, Alexandre Lacoste, Irina Rish, Nicolas Chapados, Alexandre Drouin. At Foundation Models for Time Series, 2024.

Context is Key: A Benchmark for Forecasting with Essential Textual Information. Andrew Williams, Arjun Ashok, Étienne Marcotte, Valentina Zantedeschi, Jithendaraa Subramanian, Roland Riachi, James Requeima, Alexandre Lacoste, Irina Rish, Nicolas Chapados, Alexandre Drouin. At Montreal AI Symposium (MAIS), 2024.

TACTIS-2: Better, Faster, Simpler Attentional Copulas for Multivariate Time Series. Arjun Ashok, Étienne Marcotte, Valentina Zantedeschi, Nicolas Chapados, Alexandre Drouin. At Montreal AI Symposium (MAIS), 2024.

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TACTIS-2: Better, Faster, Simpler Attentional Copulas for Multivariate Time Series. Arjun Ashok, Étienne Marcotte, Valentina Zantedeschi, Nicolas Chapados, Alexandre Drouin. At International Conference of Learning Representations (ICLR), 2024.

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Lag-Llama: A Foundation Model for Probabilistic Time Series Forecasting. Kashif Rasul, Arjun Ashok, Marin Bilos, Andrew Williams, Arian Khorasani, George Adamopoulos, Rishika Bhagwatkar, Hena Ghonia, Nadhir Hassen, Anderson Schneider, Sahil Garg, Alexandre Drouin, Nicolas Chapados, Yuriy Nevmyvaka, Irina Rish. At Workshop at the Neural Information Processing Systems (NeurIPS), 2023.