Danielle C. Maddix
- Management Science and Operations Research top 5%
- Signal Processing top 10%
- Artificial Intelligence
- Electrical and Electronic Engineering
- Building and Construction
- Co-authors
- Tim JanuschowskiJan GasthausLorenzo StellaDavid SalinasSyama Sundar RangapuramKonstantinos BenidisValentín FlunkertFrançois-Xavier Aubet
- Topics
- Stock Market Forecasting Methods (2 papers)Time Series Analysis and Forecasting (2 papers)Model Reduction and Neural Networks (2 papers)
- Partner nations
- United StatesGermanyAustria
In The Last Decade
Danielle C. Maddix
7 papers receiving 251 citations
Hit Papers
Peers
Comparison fields: 5 of 81
- Management Science and Operations Research 93
- Signal Processing 89
- Artificial Intelligence 67
- Electrical and Electronic Engineering 58
- Building and Construction 27
Countries citing papers authored by Danielle C. Maddix
This map shows the geographic impact of Danielle C. Maddix'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 Danielle C. Maddix with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Danielle C. Maddix more than expected).
Fields of papers citing papers by Danielle C. Maddix
This network shows the impact of papers produced by Danielle C. Maddix. 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 Danielle C. Maddix. The network helps show where Danielle C. Maddix may publish in the future.
Co-authorship network of co-authors of Danielle C. Maddix
This figure shows the co-authorship network connecting the top 25 collaborators of Danielle C. Maddix. A scholar is included among the top collaborators of Danielle C. Maddix based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Danielle C. Maddix. Danielle C. Maddix is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 14 | |
| 2 | 0 | |
| 3 | 10 | |
| 4 | Deep Learning for Time Series Forecasting: Tutorial and Literature Surveybreakdown → | 150 |
| 5 | GluonTS: Probabilistic and Neural Time Series Modeling in Python | 71 |
| 6 | Deep Factors for Forecasting | 6 |
| 7 | 5 | |
| 8 | 9 |
About Danielle C. Maddix
Danielle C. Maddix is a scholar working on Signal Processing, Numerical Analysis and Statistical and Nonlinear Physics, having authored 8 papers that have together received 265 indexed citations. Recurring topics across this work include Stock Market Forecasting Methods (2 papers), Time Series Analysis and Forecasting (2 papers) and Model Reduction and Neural Networks (2 papers). The work is most often cited by research in Signal Processing (89 citations), Management Science and Operations Research (93 citations) and Artificial Intelligence (67 citations). Danielle C. Maddix has collaborated with scholars based in United States, Germany and Austria. Frequent co-authors include Tim Januschowski, Jan Gasthaus, Lorenzo Stella, David Salinas, Syama Sundar Rangapuram, Konstantinos Benidis, Valentín Flunkert, François-Xavier Aubet, Michael Schneider and Laurent Callot. Their work appears in journals such as Nature Communications, Journal of Computational Physics and ACM Computing Surveys.
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.