Foundation models for time series forecasting
After months of brewing the perfect recipe in our AI kitchen, we're beyond excited to introduce Sulie - a fully managed (Model as a Service) platform for time series forecasting that actually works! From day one, we've had one mission: make powerful time series forecasting as easy as ordering your morning coffee. Now, businesses can make accurate forecasts from their data without the hassle of building complex models from scratch. We kept hearing the same frustrations from data teams trying to work with foundation models for time series forecasting: 1. "The zero-shot performance is about as…
Foundation models for time series forecasting launched on November 12, 2024 with 5 votes and more than 50% of that year's launches. The site was still online in September 2026, 1 year on.
What it does
Access to Sulie foundation models for time-series forecasting 📈 - GitHub - wearesulie/sulie: Access to Sulie foundation models for time-series forecasting 📈
Sulie offers cutting-edge foundation models for time series forecasting, enabling accurate, zero-shot predictions with minimal setup. Our transformer-based models automate the process, eliminating the need for manual training and complex configurations. pip install sulie==1.0.8 Quick Start Example After installation, initialize the SDK using your API key to start forecasting with Mimosa: from sulie import Sulie # Initialize the Sulie client client = Sulie ( api_key = "YOUR_API_KEY" ) ⚡️ Features 1. Forecasting with Mimosa Generate accurate time series forecasts using Mimosa’s zero-shot inference capabilities. This approach is ideal when you need fast, reliable predictions without training…from github.com
In the maker’s words, at launch
After months of brewing the perfect recipe in our AI kitchen, we're beyond excited to introduce Sulie - a fully managed (Model as a Service) platform for time series forecasting that actually works! From day one, we've had one mission: make powerful time series forecasting as easy as ordering your morning coffee.…
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Opper AIJul 2026 · opper.ai · ▲229Hey HN! After the Car Wash Test post got quite a big discussion going (400+ comments, https://news.ycombinator.com/item?id=47128138), I spent the past few weeks building a tool so anyone can run these kinds of questions and get structured results. No signup and free to use. You type a question, define answer options, pick up to 50 models at a time from a pool of 200+, and they all answer independently under identical conditions. No system prompt, structured output, same setup for every model. You can also run a debate round where models see each other's reasoning and get a…
Python SDK – forecasting with foundation time-series and tabular modelsDec 2025 · github.com · ▲43We’ve built a Python SDK for running inference on foundation models designed for time-series and tabular data. They are new SOTA models for time-series and tabular tasks and work out of the box. They do not require model training or feature engineering. The link to the GitHub repository is: https://github.com/S-FM/faim-python-client
FAIM: Time-Series Forecasting API Nov 2025 · faim.it.com · ▲8Hi, I’m Andrei — We’ve built FAIM, a serverless platform for running inference with foundation time-series models like Chronos2, TiRex, and FlowState. It’s built on Nvidia Triton and GCP, so you can get forecasts in seconds without managing any infrastructure. We built it because we believe the lack of accessible infrastructure for foundation time-series models is one of the main reasons these models haven’t yet spread widely in industry. To try it out, register on https://faim.it.com/ to get your API key, then run models serverlessly via our Python SDK:…
TabPFN v2 – A SOTA foundation model for small tabular data2025 · nature.com · ▲153I am excited to announce the release of TabPFN v2, a tabular foundation model that delivers state-of-the-art predictions on small datasets in just 2.8 seconds for classification and 4.8 seconds for regression compared to strong baselines tuned for 4 hours. Published in Nature, this model outperforms traditional methods on datasets with up to 10,000 samples and 500 features. The model is available under an open license: a derivative of the Apache 2 license with a single modification, adding an enhanced attribution requirement inspired by the Llama 3 license:…
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