Multi-Dimensional Regression Analysis of Time-Series Data Streams
By Yixin Chen, Guozhu Dong, Jiawei Han, Benjamin Wan-Sang Wah, Jianyong Wang. Very Large Data Bases Conference, 2002
358 citations recorded by the source corpus.
Subfields: Computer Networks and Communications, Signal Processing
View Paper: https://www.semanticscholar.org/paper/1ca6b8f5e9ec65562b781fb689cc29ff8e8e20f5
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Networks and Communications
- Philip S. Yu
- Jiawei Han
- Xifeng Yan
- Tak-Chung Fu
- Shonali Priyadarsini Krishnaswamy
- Arkady B. Zaslavsky
- Mohamed Medhat Gaber
- M. Tamer Özsu
In Signal Processing
- Jiawei Han
- Xifeng Yan
- Tak-Chung Fu
- Mohamed Medhat Gaber
- Philip S. Yu
- João Gama
- Haixun Wang
- Shonali Priyadarsini Krishnaswamy
Citations in the corpus
- Mining concept-drifting data streams using ensemble classifiers (2003, background): “Much work has been done on modeling [1], querying [2, 14, 18], and mining data streams, for instance, several papers have been published on classification [7, 21, 27], regression analysis [5], and clustering [19].”
- Mining data streams: a review (2005, background): “[12] have proposed the application of what so called regression cubes for data streams.”
- Mining Frequent Patterns in Data Streams at Multiple Time Granularities (2002, background): “The design of the tilted-time window [Chen et al.2002] is based on the fact that people are often interested in recent changes at a fine granularity, but long term changes at a coarse granularity.”
- Learning model trees from evolving data streams (2010, background): “One of the most notable and successful examples of regression on data streams is the multi-dimensional linear regression analysis of time-series data streams ( Chen et al. 2002 ).”
- A review on time series data mining (2011, methodology): “Chen et al. (2002) investigate methods for an online multi-dimensional regression analysis of time series stream data.”
- Issues in data stream management (2003, background): “Recent results in (approximate) algorithms for on-line stream mining include computing stream signatures and representative trends [21], decision trees [44], forecasting [71], k-medians clustering [16, 42], nearest neighbour queries [46], and regression analysis [18].”