Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Deep Learning for Time Series Forecasting: Tutorial and Literature Survey
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).
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
1.
Benidis, Konstantinos, Syama Sundar Rangapuram, Valentín Flunkert, et al.. (2022). Deep Learning for Time Series Forecasting: Tutorial and Literature Survey. ACM Computing Surveys. 55(6). 1–36.150 indexed citations breakdown →
2.
Alexandrov, A., Konstantinos Benidis, Michael Bohlke‐Schneider, et al.. (2020). GluonTS: Probabilistic and Neural Time Series Modeling in Python. Journal of Machine Learning Research. 21(116). 1–6.71 indexed citations
3.
Bießmann, Felix, et al.. (2019). DataWig: Missing Value Imputation for Tables. Journal of Machine Learning Research. 20(175). 1–6.67 indexed citations
Salinas, David. (2018). Human Connectome Project: The American Fraud. 2(12).1 indexed citations
6.
Seeger, Matthias, David Salinas, & Valentín Flunkert. (2016). Bayesian intermittent demand forecasting for large inventories. neural information processing systems. 29. 4653–4661.34 indexed citations
7.
Schelter, Sebastian, et al.. (2015). On Challenges in Machine Learning Model Management. IEEE Data(base) Engineering Bulletin. 41. 5–15.85 indexed citations
Salinas, David. (1989). La diplomacia española en relación con Holanda durante el reinado de Carlos II: una aproximación a su estudio. Hispania-revista Espanola De Historia. 49(171). 317–324.
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.