timeseriesAI

Creators of tsai a state-of-the-art deep learning library for time series built on top of Pytorch & fastai

[Ignacio Oguiza @ignaciooguiza]

We're thrilled to announce that #tsai has just crossed the 3k stars milestone!🚀
Started as a spin-off project during one of @jeremyphoward's fantastic fastai courses and is gaining momentum! Join us on this exciting journey! #timeseries #deeplearning https://t.co/wej9B6WqLc

[Ignacio Oguiza @ignaciooguiza]

📢New ICLR23 research paper introduces PatchTST, a state-of-the-art transformer model for multivariate #timeseries #forecasting. It processes each channel independently, breaking it into patches. Access a tutorial notebook to learn more here https://t.co/tiHybZqUWy #deeplearning

[timeseriesAI @timeseriesAI]

Stay tuned for updates and let's build better forecasts together!
https://t.co/evfSTBudnQ

[timeseriesAI @timeseriesAI]

Good news for time series enthusiasts!
Our deep learning open-source library, tsai, is getting a major upgrade over the next few weeks. We're enhancing our forecasting functionality to help you build more accurate predictions.

[timeseriesAI @timeseriesAI]

To help you take full advantage of these new features, we'll be sharing blog posts and notebooks on topics like:
✅ data preparation
✅ building a baseline
✅ forecasting with univariate or multivariate time series
✅ long-term forecasting
✅ forecasting with panel data
and more

[Ignacio Oguiza @ignaciooguiza]

Today the https://t.co/wej9B6WYAK repository has reached 1k commits. I want to thank all the users and contributors for making it possible, as well as @jeremyphoward and the fastai community for their work on fastai on which tsai is based.

[Ignacio Oguiza @ignaciooguiza]

The tsai deep learning time series library from @TimeseriesAI (https://t.co/wej9B6WqLc) has hit 1k stars this week. It is built on top of @Pytorch / @fastdotai. I am overwhelmed by the support shown by so many users 🤩

[Ignacio Oguiza @ignaciooguiza]

I just published a blog post that summarizes the best ideas from the top 15 teams (gold medal winners) #kaggle's time series competition (VPP)
https://t.co/erZ33mKwxE
@timeseriesAI