Taha Aksu

Nov 12, 2024 | salesforce.com | Taha Aksu |Gerald Woo |Juncheng Liu |Xu Liu

TL;DR: Time series forecasting is becoming increasingly important across various domains, thus having high-quality, diverse benchmarks are crucial for fair evaluation across model families. Such benchmarks also help identify model strengths and limitations, driving progressive advancements in the field. GIFT-Eval is a new comprehensive benchmark designed for evaluating general time series forecasting models, particularly foundation models.

Nov 8, 2024 | salesforce.com | Xu Liu |Juncheng Liu |Chenghao Liu |Taha Aksu

TL;DR: We propose Moirai-MoE, the first mixture-of-experts time series foundation model, achieving token-level model specialization in a data-driven manner. Extensive experiments on 39 datasets reveal that Moirai-MoE delivers up to 17% performance improvements over Moirai at the same level of model size and outperforms other time series foundation models, such as Chronos (from Amazon) and TimesFM (from Google) with up to 65 times fewer activated parameters.