Time-series analysis is one of the core pillars of statistics. However, the high dimensionality and sheer size of today’s datasets pose new statistical and algorithmic challenges.

Our project tackles these challenges while also addressing classical questions like stability and stationarity. More broadly, we aim to contribute to the modernization and expansion of the theoretical and applied foundations of time-series analysis.

Why does this matter?
Time series are everywhere:
📈 Stock markets
🛒 Sales forecasting
🌦 Weather prediction
🤖 Even text data (especially relevant in the era of ChatGPT)

A deeper understanding and more efficient, reliable models for high-dimensional time series can lead to significant advances across industries and research domains.

We’re grateful for the support from:
• Deutsche Forschungsgemeinschaft (DFG) – German Research Foundation for the funding
• University of Hamburg for continuous support
• Rainer von Sachs (Belgium) for the collaboration

Excited to get started — let’s get to work! 💪

Our grant proposal Statistical Theories for Sparse Deep Learning was selected for funding by the German Research Foundation. Thank you! We are looking forward to working on an exciting topic! 🎬

Our grant proposal A general framework for graphical models was selected for funding by the German Research Foundation. We are looking forward to working on an exciting topic! 🎬

Johannes, Jing, and David have been awarded with a grant from the UW Royalty Research Fund (RRF) for their Big Data research in Econometrics. Second hit: it pays to work with Jing and David… 🍸

Johannes, Jing (UW Econ), and David are awarded the grant “AWS Cloud Credits for Research” sponsored by Amazon! Congratulations especially to David, first year PhD student in the Stats Department and key researcher in the team.