Visual TL;DR
Time Series FragmentationDriver
From the article 4 mentionsThe promise of foundation models has largely been confined to NLP and vision, leaving the critical domain of time series forecasting in a fragmented state.
Toto 2.0 Foundation ModelsCore
new foundation models demonstrate remarkable scalability for time series
From the article 3 mentionsThe five Toto 2.0 forecasting models released under Apache 2.0 are a testament to this unified approach, setting new benchmarks in forecast quality.
Unified Scaling RecipeCore
single training approach effective across millions to billions of parameters
Apache 2.0 ReleaseEffect
From the articleThe five Toto 2.0 forecasting models released under Apache 2.0 are a testament to this unified approach, setting new benchmarks in forecast quality.
Consistent Quality GainsEffect
forecast quality improves reliably with increased model parameter size
From the articleThis work demonstrates that time series models, much like their counterparts in other domains, exhibit remarkable scalability, with a single training recipe yielding consistent forecast quality gains from millions to billions of parameters.
Practical FrameworkEffect
codified insights into a usable and accessible framework for researchers
From the articleThe researchers behind Toto 2.0 have codified this insight into a practical framework.
State-of-the-Art PerformanceOutcome
achieving new benchmarks across multiple forecasting benchmarks
From the articleThe Toto 2.0 forecasting models have established new state-of-the-art results on three distinct forecasting benchmarks: BOOM (observability), GIFT-Eval (general-purpose), and the contamination-resistant TIME benchmark.
