Tim Januschowski

3.3k citations
33 papers · 1.3k · 2 hit papers · h-index 16

Impact in

Papers in

Tim Januschowski

33 papers receiving 1.2k citations

Tim Januschowski's Hit Papers

Deep Learning for Time Series Forecasting: Tutorial and Literature Survey 2022 · 179 citations
1790+2+5Years since publication50100150200250

Peers

Tim Januschowski
Comparison fields: 5 of 110
  • Management Science and Operations Research 563
  • Signal Processing 431
  • Artificial Intelligence 362
  • Computational Mathematics 5
  • Building and Construction 111
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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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authors

The 25 scholars most cited alongside Tim Januschowski, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Tim Januschowski Line = papers co-authored together Tim Januschowski links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 33 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Deep State Space Models for Time Series Forecasting
Hit paper breakdown →
2018268
2
Deep Learning for Time Series Forecasting: Tutorial and Literature Survey
Hit paper breakdown →
2022179
3 2019145
4
On Challenges in Machine Learning Model Management
201590
5 201787
6
GluonTS: Probabilistic and Neural Time Series Modeling in Python
202082
7 202163
8
Probabilistic Forecasting with Spline Quantile Function RNNs
201948
9
Normalizing Kalman Filters for Multivariate Time Series Analysis
202047
10 201846
11
End-to-End Learning of Coherent Probabilistic Forecasts for Hierarchical Time Series
202124
12 201324
13 202119
14 201918
15 202117
16 201917
17 201114
18
Deep Learning for Forecasting: Current Trends and Challenges
201811
19 202011
20 20139

About Tim Januschowski

Tim Januschowski is a scholar working on Signal Processing, Management Science and Operations Research, Artificial Intelligence, Computer Networks and Communications and Information Systems, having authored 33 papers that have together received 1.3k indexed citations. Recurring topics across this work include Time Series Analysis and Forecasting (15 papers), Stock Market Forecasting Methods (12 papers), Forecasting Techniques and Applications (10 papers), Data Stream Mining Techniques (4 papers), Advanced Database Systems and Queries (3 papers), Computational Physics and Python Applications (3 papers), Cloud Computing and Resource Management (3 papers) and Neural Networks and Applications (3 papers). The work is most often cited by research in Management Science and Operations Research (563 citations), Signal Processing (431 citations), Artificial Intelligence (362 citations), Computational Mathematics (5 citations) and Building and Construction (111 citations). Tim Januschowski has collaborated with scholars based in Germany, United States and Switzerland. Frequent co-authors include Jan Gasthaus, Yuyang Wang, Syama Sundar Rangapuram, David Salinas, Lorenzo Stella, Valentín Flunkert, Matthias Seeger, Laurent Callot, Konstantinos Benidis and Michael Bohlke‐Schneider. Their work appears in journals such as Proceedings of the VLDB Endowment, International Journal of Forecasting, Discrete Optimization, 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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