Time series forecasting is becoming increasingly important across various domains, thus having high-quality, diverse benchmarks are crucial for fair evaluation across model families.
We propose Moirai-MoE, the first mixture-of-experts time series foundation model, achieving token-level model specialization in a data-driven manner.
TL;DR: We introduce INDICT, a novel framework that empowers Large Language Models (LLMs) with Internal Dialogues of Critiques for both safety and helpfulness guidance. The internal dialogue is a dual cooperative system between…
TL;DR: Moirai is a cutting-edge time series foundation model, offering universal forecasting capabilities. It stands out as a versatile time series forecasting model capable of addressing diverse forecasting tasks across multiple domains, frequencies,…
TL;DR: With CodeChain, a pretrained large language model (LLM) can solve challenging coding problems by integrating modularity in generation samples and self-improve by employing a chain of self-revisions on representative sub-modules. CodeChain can…
AUTHORS: Chenghao Liu, Quang Pham, Doyen Sahoo, Donald Rose TL;DR: Nonstationary data, which changes its statistical properties over time, can make time series forecasting difficult. Despite the recent success of deep learning techniques…
AUTHORS: Huan Wang, Aadyot Bhatnagar, Doyen Sahoo, Wenzhuo Yang, Steven Hoi, Caiming Xiong, Donald Rose TL;DR: Time series data is a critical source of insights for many applications, including IT Operations, Quality Management,…
