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.
This map shows the geographic impact of Jan Gasthaus'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 Jan Gasthaus with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jan Gasthaus more than expected).
This network shows the impact of papers produced by Jan Gasthaus. 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 Jan Gasthaus. The network helps show where Jan Gasthaus may publish in the future.
Co-authorship network of co-authors of Jan Gasthaus
This figure shows the co-authorship network connecting the top 25 collaborators of Jan Gasthaus.
A scholar is included among the top collaborators of Jan Gasthaus 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 Jan Gasthaus. Jan Gasthaus 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.
Aubet, François-Xavier, et al.. (2022). Neural Contextual Anomaly Detection for Time Series. Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence. 2843–2851.41 indexed citations
3.
Januschowski, Tim, et al.. (2021). Probabilistic Forecasting: A Level-Set Approach. Neural Information Processing Systems. 34.3 indexed citations
4.
Januschowski, Tim, et al.. (2021). Forecasting with trees. International Journal of Forecasting. 38(4). 1473–1481.52 indexed citations
5.
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
6.
Rangapuram, Syama Sundar, et al.. (2020). Deep Rao-Blackwellised Particle Filters for Time Series Forecasting. Neural Information Processing Systems. 33. 15371–15382.8 indexed citations
Wang, Yuyang, Alex Smola, Danielle C. Maddix, et al.. (2019). Deep Factors for Forecasting. International Conference on Machine Learning. 6607–6617.6 indexed citations
9.
Faloutsos, Christos, Valentín Flunkert, Jan Gasthaus, Tim Januschowski, & Yuyang Wang. (2019). Forecasting Big Time Series. 3209–3210.14 indexed citations
10.
Januschowski, Tim, Jan Gasthaus, Yuyang Wang, et al.. (2019). Criteria for classifying forecasting methods. International Journal of Forecasting. 36(1). 167–177.117 indexed citations
11.
Januschowski, Tim, Jan Gasthaus, & Yuyang Wang. (2019). Open-source forecasting tools in Python. RePEc: Research Papers in Economics.3 indexed citations
Januschowski, Tim, Jan Gasthaus, Yuyang Wang, Syama Sundar Rangapuram, & Laurent Callot. (2018). Deep Learning for Forecasting: Current Trends and Challenges. RePEc: Research Papers in Economics. 42–47.12 indexed citations
15.
Januschowski, Tim, Jan Gasthaus, Syama Sundar Rangapuram, & Laurent Callot. (2018). Deep Learning for Forecasting. RePEc: Research Papers in Economics. 35–41.3 indexed citations
16.
Wood, Frank, Jan Gasthaus, Cédric Archambeau, Lancelot F. James, & Yee Whye Teh. (2011). The sequence memoizer. Communications of the ACM. 54(2). 91–98.26 indexed citations
17.
Gasthaus, Jan & Yee Whye Teh. (2010). Improvements to the Sequence Memoizer. UCL Discovery (University College London). 23. 685–693.10 indexed citations
Gasthaus, Jan, Frank Wood, Dilan Görür, & Yee Whye Teh. (2008). Dependent Dirichlet Process Spike Sorting. UCL Discovery (University College London). 21. 497–504.21 indexed citations
20.
Degen, Judith, et al.. (2007). FIASCO: Filtering the Internet by Automatic Subtree Classification, Osnabr¨ uck.3 indexed citations
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.