Dima Alberg
Impact in
- Finance top 5%
- Financial Risk and Volatility Modeling
- Financial Markets and Investment Strategies
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- Stock Market Forecasting Methods
Papers in
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- Time Series Analysis and Forecasting 4
- Data Management and Algorithms 2
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- Data Stream Mining Techniques 3
- Co-authors
- Rami Yosef (1 shared paper)Haim Shalit (1 shared paper)Mark Last (5 shared papers)Abraham Kandel (1 shared paper)Yossi Hadad (1 shared paper)Baruch Keren (1 shared paper)
- Journals
- Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery (1 paper)Energies (1 paper)Applied Financial Economics (1 paper)International Journal of Database Theory and Application (1 paper)Vietnam Journal of Computer Science (1 paper)
- Partner nations
- IsraelUnited States
In The Last Decade
Dima Alberg
10 papers receiving 289 citations
Peers
Comparison fields: 5 of 53
- Finance 162
- Management Science and Operations Research 85
- Economics and Econometrics 177
- General Economics, Econometrics and Finance 52
- Artificial Intelligence 50
Countries citing papers authored by Dima Alberg
This map shows the geographic impact of Dima Alberg'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 Dima Alberg with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dima Alberg more than expected).
Fields of papers citing papers by Dima Alberg
This network shows the impact of papers produced by Dima Alberg. 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 Dima Alberg. The network helps show where Dima Alberg may publish in the future.
Co-authors
The 6 scholars most cited alongside Dima Alberg, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2008 | 191 | |
| 2 | 2018 | 61 | |
| 3 | 2011 | 30 | |
| 4 | 2007 | 30 | |
| 5 | 2008 | 2 | |
| 6 | 2009 | 2 | |
| 7 | 2015 | 2 | |
| 8 | 2023 | 1 | |
| 9 | 2021 | 1 | |
| 10 | 2015 | 1 | |
| 11 | 2019 | 0 |
About Dima Alberg
Dima Alberg is a scholar working on Signal Processing, Artificial Intelligence, Information Systems, Management Information Systems and Management Science and Operations Research, having authored 11 papers that have together received 321 indexed citations. Recurring topics across this work include Time Series Analysis and Forecasting (4 papers), Data Mining Algorithms and Applications (3 papers), Data Stream Mining Techniques (3 papers), Data Management and Algorithms (2 papers), Stock Market Forecasting Methods (2 papers), Complex Systems and Time Series Analysis (1 paper), Electric Vehicles and Infrastructure (1 paper) and Supply Chain and Inventory Management (1 paper). The work is most often cited by research in Finance (162 citations), Management Science and Operations Research (85 citations), Economics and Econometrics (177 citations), General Economics, Econometrics and Finance (52 citations) and Artificial Intelligence (50 citations). Dima Alberg has collaborated with scholars based in Israel and United States. Frequent co-authors include Rami Yosef, Haim Shalit, Mark Last, Abraham Kandel, Yossi Hadad and Baruch Keren. Their work appears in journals such as Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery, Energies, Applied Financial Economics, International Journal of Database Theory and Application and Vietnam Journal of Computer Science.
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.