A5088778116- Time Series Analysis and Forecasting
- Anomaly Detection Techniques and Applications
- Statistical Methods and Inference
- Stock Market Forecasting Methods
- Statistical Methods and Bayesian Inference
- Spectroscopy and Chemometric Analyses
- Currency Recognition and Detection
- Advanced Causal Inference Techniques
Paul Scherrer Institute
2021-2023
Particle accelerators are complex facilities that produce large amounts of structured data and have clear optimization goals as well precisely defined control requirements. As such they naturally amenable to data-driven research methodologies. The from sensors monitors inside the accelerator form multivariate time series. With fast preemptive approaches being highly preferred in diagnostics, application series forecasting methods is particularly promising. This review formulates problem...
The beam interruptions (interlocks) of particle accelerators, despite being necessary safety measures, lead to abrupt operational changes and a substantial loss time. A novel time series classification approach is applied decrease in the High-Intensity Proton Accelerator complex by forecasting interlock events. performed through binary windows multivariate series. are transformed into Recurrence Plots which then classified Convolutional Neural Network, not only captures inner structure but...
Many practical studies in biology, medicine, behavior science and the social sciences seek to establish causal relationship between treatments outcomes, rather than mere associations. In this paper, we use a graphical model describe study its identification. For an unidentifiable model, introduce covariates which are always observed into so that it becomes identifiable. We then give identifiable condition of prove mathematically. Finally, <img src=image/abs1.png></img>algorithm for average...