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.
Countries citing papers authored by Tim Januschowski
Since
Specialization
Citations
This map shows the geographic impact of Tim Januschowski'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 Tim Januschowski with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tim Januschowski more than expected).
Fields of papers citing papers by Tim Januschowski
This network shows the impact of papers produced by Tim Januschowski. 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 Tim Januschowski. The network helps show where Tim Januschowski may publish in the future.
Co-authorship network of co-authors of Tim Januschowski
This figure shows the co-authorship network connecting the top 25 collaborators of Tim Januschowski.
A scholar is included among the top collaborators of Tim Januschowski 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 Tim Januschowski. Tim Januschowski 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.
Januschowski, Tim, et al.. (2021). Probabilistic Forecasting: A Level-Set Approach. Neural Information Processing Systems. 34.3 indexed citations
Januschowski, Tim, et al.. (2021). Forecasting with trees. International Journal of Forecasting. 38(4). 1473–1481.52 indexed citations
6.
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
7.
Bézenac, Emmanuel de, Syama Sundar Rangapuram, Konstantinos Benidis, et al.. (2020). Normalizing Kalman Filters for Multivariate Time Series Analysis. Neural Information Processing Systems. 33. 2995–3007.44 indexed citations
8.
Flunkert, Valentín, et al.. (2020). A Simple and Effective Predictive Resource Scaling Heuristic for Large-scale Cloud Applications.. Very Large Data Bases.1 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
11.
Faloutsos, Christos, Valentín Flunkert, Jan Gasthaus, Tim Januschowski, & Yuyang Wang. (2019). Forecasting Big Time Series. 3209–3210.14 indexed citations
12.
Kolassa, Stephan & Tim Januschowski. (2018). A Classification of Business Forecasting Problems. 36–43.7 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.
Schelter, Sebastian, et al.. (2015). On Challenges in Machine Learning Model Management. IEEE Data(base) Engineering Bulletin. 41. 5–15.85 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.