Show HN: Foundation models for time series forecasting

(github.com)

5 points by TechieVoyager42 2 days ago | 2 comments

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 reliable as a chocolate teapot!" 2. "Fine-tuning these models? Easier to teach a cat to bark!" 3. "And don't get me started on covariate support..."

What makes Sulie special? • Automated model fine-tuning using LoRA - no PhD required! • Full covariate support for more accurate predictions. • Go from zero to production-ready forecasts in minutes (not weeks). • Zero ML complexity - we handle all MLOps heavy-lifting (you focus on the insights).

We're already working with amazing customers who are: • Optimizing their supply chains • Making precise financial forecasts • Building custom models using Sulie's powerful embeddings

And this is just the beginning! Stay tuned for deep dives into these use cases in the coming weeks

Check us out: Python SDK: https://github.com/wearesulie/sulie Website: https://sulie.co

namanyayg a day ago | next |

nice to see someone tackling the pain points of foundation models for time series forecasting.

i'm curious, how do you handle concept drift in the data? do you have any built-in mechanisms for detecting when the model needs to be re-trained or updated, or is that something the user needs to handle manually?