Streaming Pattern Discovery in Multiple Time-Series
By Spiros E. Papadimitriou, Jimeng Sun, Christos Faloutsos. Very Large Data Bases Conference, 2005
430 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Signal Processing
View Paper: https://www.semanticscholar.org/paper/3ffc2ef8b918872e6385c09362ccd76f40fd07c6
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Charalampos E. Tsourakakis
- Jimeng Sun
- Dacheng Tao
- Christos Faloutsos
- Tak-Chung Fu
- Haiping Lu
- Laura Balzano
- K. Plataniotis
In Signal Processing
- Tak-Chung Fu
- Charalampos E. Tsourakakis
- Haiping Lu
- K. Plataniotis
- Anastasios N. Venetsanopoulos
- Laura Balzano
- Dacheng Tao
- Jimeng Sun
Citations in the corpus
- Deep Learning for Time Series Anomaly Detection: A Survey (2022, neutral): “For the case of high-dimensional anomaly detection, the proposed method can be used by dimensionality reduction techniques, such as principle component analysis (PCA) to detect anomalies [87], [139].”
- Beyond streams and graphs: dynamic tensor analysis (2006, methodology): “Then we can do multivariate analysis or SVD [25], sketches and random projections [14] to .nd patterns and outliers.”
- Online identification and tracking of subspaces from highly incomplete information (2010, background): “Environmental monitoring of soil and crop conditions [10], water contamination [15], and seismological activity [18] have all been demonstrated to be efficiently summarized by very low-dimensional subspace representations.”
- A review on time series data mining (2011, methodology): “Papadimitriou et al. (2005) introduce a streaming pattern discovery method in multiple time series, which summarizes the key trends in the stream collection based on PCA.”
- MACH: Fast Randomized Tensor Decompositions (2009, methodology): “, the two heads method which uses a Tucker decomposition and treats the time aspect using wavelets [31, 18, 36].”
- A survey of multilinear subspace learning for tensor data (2011, methodology): “Three variants of ITA are introduced in [17]: dynamic tensor analysis (DTA) that incrementally maintains covariance matrices for all modes and uses the leading eigenvectors of covariance matrices as projection matrices [15], streaming tensor analysis (STA) that directly updates the leading eigenvect”