Automated Author ProfileSchüssler, Rainer Alexander
Schüssler, Rainer Alexander
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 1.3 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
We propose a time series forecasting method designed to effectively handle large sets of predictive signals, many of which may be irrelevant or short-lived over time. Our method transforms predictive signals into candidate density forecasts via time-varying coefficient models, and subsequently combines them into an aggregate density forecast via time-varying subset combination. The approach is computationally efficient because it uses online prediction and updating. Through extensive simulation analysis, we find that our approach outperforms competitive benchmark methods in terms of forecast accuracy and computing time. We further demonstrate the capabilities of our method in applications to forecasting aggregate daily stock returns and quarterly inflation.
Authors
- Adämmer, Philipp ;
- Lehmann, Sven ;
- Schüssler, Rainer Alexander
We propose a time series forecasting method designed to effectively handle large sets of predictive signals, many of which may be irrelevant or short-lived over time. Our method transforms predictive signals into candidate density forecasts via time-varying coefficient models, and subsequently combines them into an aggregate density forecast via time-varying subset combination. The approach is computationally efficient because it uses online prediction and updating. Through extensive simulation analysis, we find that our approach outperforms competitive benchmark methods in terms of forecast accuracy and computing time. We further demonstrate the capabilities of our method in applications to forecasting aggregate daily stock returns and quarterly inflation.
Authors
- Adämmer, Philipp ;
- Lehmann, Sven ;
- Schüssler, Rainer Alexander