Adrian Trapletti
Impact in
- Finance top 10%
- Financial Risk and Volatility Modeling
- Stochastic processes and financial applications
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- Stock Market Forecasting Methods
- Forecasting Techniques and Applications
Papers in
-
- Neural Networks and Applications 3
-
- Market Dynamics and Volatility 2
- Co-authors
- Kurt Hornik (5 shared papers)Friedrich Leisch (4 shared papers)Fulvio Corsi (1 shared paper)Gilles Zumbach (1 shared paper)Alois Geyer (1 shared paper)Urs Boutellier (1 shared paper)Christina M. Spengler (1 shared paper)
- Journals
- Neural Computation (1 paper)Journal of Forecasting (1 paper)European Journal of Applied Physiology (1 paper)Neural Information Processing Systems (1 paper)SSRN Electronic Journal (1 paper)
- Partner nations
- AustriaSwitzerlandItaly
In The Last Decade
Adrian Trapletti
8 papers receiving 131 citations
Peers
Comparison fields: 5 of 61
- Finance 37
- Management Science and Operations Research 30
- Economics and Econometrics 53
- General Economics, Econometrics and Finance 16
- Complementary and alternative medicine 11
Countries citing papers authored by Adrian Trapletti
This map shows the geographic impact of Adrian Trapletti's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Adrian Trapletti with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Adrian Trapletti more than expected).
Fields of papers citing papers by Adrian Trapletti
This network shows the impact of papers produced by Adrian Trapletti. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Adrian Trapletti. The network helps show where Adrian Trapletti may publish in the future.
Co-authors
The 7 scholars most cited alongside Adrian Trapletti, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2000 | 40 | |
| 2 | Time Series Analysis and Computational Finance | 2015 | 22 |
| 3 | 2000 | 20 | |
| 4 | 2002 | 20 | |
| 5 | 2002 | 15 | |
| 6 | Time Series Analysis and Computational Finance [R package tseries version 0.10-48] | 2020 | 11 |
| 7 | Stationarity and Stability of Autoregressive Neural Network Processes | 1998 | 10 |
| 8 | 1998 | 5 |
About Adrian Trapletti
Adrian Trapletti is a scholar working on Artificial Intelligence, Economics and Econometrics, Control and Systems Engineering, Finance and Signal Processing, having authored 8 papers that have together received 143 indexed citations. Recurring topics across this work include Neural Networks and Applications (3 papers), Stock Market Forecasting Methods (2 papers), Control Systems and Identification (2 papers), Market Dynamics and Volatility (2 papers), Financial Risk and Volatility Modeling (2 papers), Stochastic processes and financial applications (1 paper), Blind Source Separation Techniques (1 paper) and Cardiovascular and exercise physiology (1 paper). The work is most often cited by research in Finance (37 citations), Management Science and Operations Research (30 citations), Economics and Econometrics (53 citations), General Economics, Econometrics and Finance (16 citations) and Complementary and alternative medicine (11 citations). Adrian Trapletti has collaborated with scholars based in Austria, Switzerland and Italy. Frequent co-authors include Kurt Hornik, Friedrich Leisch, Fulvio Corsi, Gilles Zumbach, Alois Geyer, Urs Boutellier and Christina M. Spengler. Their work appears in journals such as Neural Computation, Journal of Forecasting, European Journal of Applied Physiology, Neural Information Processing Systems and SSRN Electronic Journal.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.