David Salinas

29 papers receiving 842 citations

Hit Papers

Deep Learning for Time Series Forecasting: Tutorial and L...2022202620232024202250100150

Peers

David Salinas
Comparison fields: 5 of 121
  • Management Science and Operations Research 302
  • Artificial Intelligence 245
  • Signal Processing 216
  • Electrical and Electronic Engineering 131
  • Computer Vision and Pattern Recognition 71
Replace Floriana Esposito with:
Floriana Esposito Italy
Fei Gao China
Banavar Sridhar United States
Qiang Guo China
Ming S. Hung United States
Manjusha Pandey India
Valentina Casola Italy
Yi Zhou China
David Salinas relative to Floriana Esposito Italy Floriana Esposito's profile →
Citations per field
00.5×4.1×
Floriana Esposito · 1×
Citations per year

Countries citing papers authored by David Salinas

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authorship network of co-authors of David Salinas

This figure shows the co-authorship network connecting the top 25 collaborators of David Salinas. A scholar is included among the top collaborators of David Salinas 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 David Salinas. David Salinas is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
1
Deep Learning for Time Series Forecasting: Tutorial and Literature Surveybreakdown →
150
2
GluonTS: Probabilistic and Neural Time Series Modeling in Python
71
3
DataWig: Missing Value Imputation for Tables
67
4 46
5
Human Connectome Project: The American Fraud
1
6
Bayesian intermittent demand forecasting for large inventories
34
7
On Challenges in Machine Learning Model Management
85
8 51
9 0
10
Adaptive sensor networks for mobile target localization and tracking
1
11 31
12 16
13 1
14 0
15
La diplomacia española en relación con Holanda durante el reinado de Carlos II: una aproximación a su estudio
0
16 0
17 1
18 11
19 27
20 3

About David Salinas

David Salinas is a scholar working on Conservation, Signal Processing and Management Science and Operations Research, having authored 39 papers that have together received 912 indexed citations. Recurring topics across this work include Neuroethics, Human Enhancement, Biomedical Innovations (5 papers), Time Series Analysis and Forecasting (4 papers) and Forecasting Techniques and Applications (4 papers). The work is most often cited by research in Management Science and Operations Research (302 citations), Signal Processing (216 citations) and Computer Graphics and Computer-Aided Design (30 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 Journal of Applied Mechanics, ACM Computing Surveys and Journal of Machine Learning Research.

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.

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