David Salinas
Impact in
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- Stock Market Forecasting Methods
- Forecasting Techniques and Applications
- Signal Processing top 5%
- Time Series Analysis and Forecasting
Papers in
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- Machine Learning and Data Classification 3
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- Metal Forming Simulation Techniques 3
- Co-authors
- Tim Januschowski (6 shared papers)Valentín Flunkert (6 shared papers)Jan Gasthaus (6 shared papers)Sebastian Schelter (4 shared papers)Laurent Callot (3 shared papers)Felix Bießmann (3 shared papers)Michael Bohlke‐Schneider (3 shared papers)Dustin Lange (3 shared papers)
- Journals
- Maderas Ciencia y tecnología (3 papers)Journal of Machine Learning Research (2 papers)Journal of Composite Materials (2 papers)Computer Graphics Forum (1 paper)Nuclear Science and Engineering (1 paper)
- Partner nations
- United StatesGermanyPeru
In The Last Decade
David Salinas
32 papers receiving 986 citations
David Salinas's Hit Papers
Peers
Comparison fields: 5 of 122
- Management Science and Operations Research 357
- Signal Processing 248
- Computer Graphics and Computer-Aided Design 35
- Artificial Intelligence 278
- Building and Construction 75
Countries citing papers authored by David Salinas
This map shows the geographic impact of David Salinas'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 David Salinas with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Salinas more than expected).
Fields of papers citing papers by David Salinas
This network shows the impact of papers produced by David Salinas. 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 David Salinas. The network helps show where David Salinas may publish in the future.
Co-authors
The 25 scholars most cited alongside David Salinas, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 42 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Deep Learning for Time Series Forecasting: Tutorial and Literature Survey Hit paper breakdown → | 2022 | 179 |
| 2 | 2019 | 145 | |
| 3 | On Challenges in Machine Learning Model Management | 2015 | 90 |
| 4 | 2017 | 87 | |
| 5 | GluonTS: Probabilistic and Neural Time Series Modeling in Python | 2020 | 82 |
| 6 | DataWig: Missing Value Imputation for Tables | 2019 | 69 |
| 7 | 2015 | 58 | |
| 8 | 2018 | 52 | |
| 9 | Probabilistic Forecasting with Spline Quantile Function RNNs | 2019 | 48 |
| 10 | 2012 | 36 | |
| 11 | Bayesian intermittent demand forecasting for large inventories | 2016 | 34 |
| 12 | 1972 | 34 | |
| 13 | 1972 | 31 | |
| 14 | 2019 | 25 | |
| 15 | 2012 | 22 | |
| 16 | 2009 | 18 | |
| 17 | 1972 | 14 | |
| 18 | 1991 | 9 | |
| 19 | 1994 | 7 | |
| 20 | 2024 | 5 |
About David Salinas
David Salinas is a scholar working on Artificial Intelligence, Mechanical Engineering, Signal Processing, Management Science and Operations Research and Mechanics of Materials, having authored 42 papers that have together received 1.1k indexed citations. Recurring topics across this work include Neuroethics, Human Enhancement, Biomedical Innovations (5 papers), Time Series Analysis and Forecasting (4 papers), Forecasting Techniques and Applications (4 papers), Stock Market Forecasting Methods (3 papers), Metal Forming Simulation Techniques (3 papers), Machine Learning and Data Classification (3 papers), Historical Studies on Spain (3 papers) and Vector-borne infectious diseases (2 papers). The work is most often cited by research in Management Science and Operations Research (357 citations), Signal Processing (248 citations), Computer Graphics and Computer-Aided Design (35 citations), Artificial Intelligence (278 citations) and Building and Construction (75 citations). David Salinas has collaborated with scholars based in United States, Germany and Peru. Frequent co-authors include Tim Januschowski, Valentín Flunkert, Jan Gasthaus, Sebastian Schelter, Laurent Callot, Felix Bießmann, Michael Bohlke‐Schneider, Dustin Lange, Syama Sundar Rangapuram and Konstantinos Benidis. Their work appears in journals such as Maderas Ciencia y tecnología, Journal of Machine Learning Research, Journal of Composite Materials, Computer Graphics Forum and Nuclear Science and Engineering.
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.