A symbolic representation of time series, with implications for streaming algorithms

  title={A symbolic representation of time series, with implications for streaming algorithms},
  author={Jessica Lin and Eamonn J. Keogh and S. Lonardi and B. Chiu},
  booktitle={DMKD '03},
  • Jessica Lin, Eamonn J. Keogh, +1 author B. Chiu
  • Published in DMKD '03 2003
  • Computer Science
  • The parallel explosions of interest in streaming data, and data mining of time series have had surprisingly little intersection. This is in spite of the fact that time series data are typically streaming data. The main reason for this apparent paradox is the fact that the vast majority of work on streaming data explicitly assumes that the data is discrete, whereas the vast majority of time series data is real valued.Many researchers have also considered transforming real valued time series into… CONTINUE READING
    1,672 Citations
    Experiencing SAX: a novel symbolic representation of time series
    • 1,173
    • PDF
    Mining Time Series Data
    • 116
    • PDF
    Towards Optimal Symbolization for Time Series Comparisons
    • 1
    • PDF
    Feature-Based Dividing Symbolic Time Series Representation for Streaming Data Processing
    • 2
    Towards a Faster Symbolic Aggregate Approximation Method
    • 9
    • PDF
    Genetic Algorithms-Based Symbolic Aggregate Approximation
    • 24
    The Parallel and Distributed Future of Data Series Mining
    • Themis Palpanas
    • Computer Science
    • 2017 International Conference on High Performance Computing & Simulation (HPCS)
    • 2017
    • 15


    Fast Subsequence Matching in Time-Series Databases
    • Clu-istos Foutsos, M. llanganatan, Yanais Maaolopoulo
    • 1994
    • 1,339
    • Highly Influential
    • PDF
    TSA-tree: a wavelet-based approach to improve the efficiency of multi-level surprise and trend queries on time-series data
    • C. Shahabi, X. Tian, W. Zhao
    • Computer Science
    • Proceedings. 12th International Conference on Scientific and Statistica Database Management
    • 2000
    • 133
    • Highly Influential
    Efficient time series matching by wavelets
    • K. Chan, A. Fu
    • Computer Science
    • Proceedings 15th International Conference on Data Engineering (Cat. No.99CB36337)
    • 1999
    • 1,153
    • Highly Influential
    • PDF
    Distance measures for effective clustering of ARIMA time-series
    • 393
    • Highly Influential
    • PDF
    Fast Time Sequence Indexing for Arbitrary Lp Norms
    • 707
    • Highly Influential
    • PDF
    Finding motifs using random projections
    • 539
    • Highly Influential
    • PDF
    Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids
    • 4,313
    • Highly Influential
    • PDF
    Novelty detection in time series data using ideas from immunology
    • 300
    • Highly Influential
    • PDF
    Finding Motifs in Time Series. In proceedings of the 2 Workshop on Temporal Data Mining, at the 8 ACM SIGKDD Int
    • l Conference on Knowledge Discovery and Data Mining
    • 2002