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🚀 Excited to share our new work! Tackling time series confounders with RioT: 🔄 Feedback across time & frequency 🎯 Steers models to right reasons 📊 Tested on our novel production line dataset with natural confounding factors arxiv.org/abs/2402.12921 huggingface.co/datasets/AIML-…

Our new XIT method pretrains 🏋️ on 75 time series 📈 datasets at once! 🔗: XD-MixUp interpolates between datasets 🧮: New SICC loss proposed 📊: Competitive on 5 baselines 🤝 Work with Maurice Kraus @ NeurIPS, David Steinmann, Devendra Singh Dhami | देवेंद्र सिंह धामी 🇮🇳 & Kristian Kersting! arxiv.org/abs/2402.15404

xLSTM for multivar. time series: arxiv.org/abs/2410.16928. By Kristian Kersting, one of Germany's top AI researchers. xLSTM excels in long-term forecasting. "xLSTM-Mixer consistently achieved state-of-the-art performance, outperforming previous methods in 41 out of 56 cases." Wow

🙏for the kind words. I am standing on the shoulders of giants 🦾 xLSTMs are amazing and work really well for multivariate time series. All the credit to the amazing team Maurice Kraus @ NeurIPS Felix Divo Devendra Singh Dhami | देवेंद्र सिंह धामी 🇮🇳

🚀 I'm very honored that Sepp Hochreiter mentioned our paper! Thrilled to present **xLSTM-Mixer**—a novel approach for multivariate time series forecasting that achieves SOTA performance on long-term forecasting tasks. Let me walk you through what makes it stand out.🧵👇

My college Maurice Kraus @ NeurIPS just summarized really nicely what our new xLSTM-Mixer architecture is all about!

We updated our ICLR dataset (see github.com/berenslab/iclr…) with blind 2025 submissions to ICLR 2026. Over 10k submissions this year. I like how this embedding (SBERT + tSNE) shows which ML areas are old-school and which ones are currently booming.

It was great to compile this with y'all together! Feel free to reach out to us at NeurIPS Conference! We're here and welcome any feedback - ensuring to cover all aspects of the topic. Looking forward to meeting you!

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xLSTM for time series with Granger causalityies: arxiv.org/abs/2502.09981 xLSTM again shows superb performance at time series analysis. "Our experimental evaluations on three datasets demonstrate the overall efficacy of our proposed GC-xLSTM model."

Forecasting Company Fundamentals Felix Divo, Eric Endress, Kevin Endler, Kristian Kersting, Devendra Singh Dhami. Action editor: Yan Liu. openreview.net/forum?id=haf78… #forecasting #forecasts #stock