Mining the stock market (extended abstract): which measure is best?
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Time series similarity measures (tutorial PM-2)
Tutorial notes of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
What Is the Nearest Neighbor in High Dimensional Spaces?
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Fast Time Sequence Indexing for Arbitrary Lp Norms
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Haar Wavelets for Efficient Similarity Search of Time-Series: With and Without Time Warping
IEEE Transactions on Knowledge and Data Engineering
Efficient Time Series Matching by Wavelets
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Landmarks: A New Model for Similarity-Based Pattern Querying in Time Series Databases
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Clustering of streaming time series is meaningless
DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Online Amnesic Approximation of Streaming Time Series
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Efficient Similarity Search in Streaming Time Sequences
SSDBM '04 Proceedings of the 16th International Conference on Scientific and Statistical Database Management
Multi-dimensional regression analysis of time-series data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
A framework for clustering evolving data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Efficient range-constrained similarity search on wavelet synopses over multiple streams
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
A fast approximation strategy for summarizing a set of streaming time series
Proceedings of the 2010 ACM Symposium on Applied Computing
Pattern recognition in stock data based on a new segmentation algorithm
KSEM'07 Proceedings of the 2nd international conference on Knowledge science, engineering and management
Adaptive dimensionality reduction for recent-biased time series analysis
Proceedings of the 1st Amrita ACM-W Celebration on Women in Computing in India
A review on time series data mining
Engineering Applications of Artificial Intelligence
Relevance analysis of stochastic biosignals for identification of pathologies
EURASIP Journal on Advances in Signal Processing - Special issue on biologically inspired signal processing: analyses, algorithms and applications
Enhancing DWT for recent-biased dimension reduction of time series data
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Recentness biased learning for time series forecasting
Information Sciences: an International Journal
A prediction framework based on contextual data to support Mobile Personalized Marketing
Decision Support Systems
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Recent-biased approximations have received increased attention recently as a mechanism for learning trend patterns from time series or data streams. They have shown promise for clustering time series and incrementally pattern maintaining. In this paper, we design a generalized dimension-reduction framework for recent-biased approximations, aiming at making traditional dimension-reduction techniques actionable in recent-biased time series analysis. The framework is designed in two ways: equi-segmented scheme and vari-segmented scheme. In both schemes, time series data are first partitioned into segments and a dimension-reduction technique is applied to each segment. Then, more coefficients are kept for more recent data while fewer kept for older data. Thus, more details are preserved for recent data and fewer coefficients are kept for the whole time series, which improves the efficiency greatly. We experimentally evaluate the proposed approach, and demonstrate that traditional dimension-reduction techniques, such as SVD, DFT, DWT, PIP, PAA, and landmarks, can be embedded into our framework for recent-biased approximations over streaming time series.