A Multi-resolution Approximation for Time Series

被引:0
|
作者
Heider Sanchez
Benjamin Bustos
机构
[1] University of Chile,Department of Computer Science
来源
Neural Processing Letters | 2020年 / 52卷
关键词
Time series; Multi-resolution representation; Classification; Discord discovery;
D O I
暂无
中图分类号
学科分类号
摘要
Time series is a common and well-known way for describing temporal data. However, most of the state-of-the-art techniques for analysing time series have focused on generating a representation for a single level of resolution. For analysing of a time series at several levels of resolutions, one would require to compute different representations, one for each resolution level. We introduce a multi-resolution representation for time series based on local trends and mean values. We require the level of resolution as parameter, but it can be automatically computed if we consider the maximum resolution of the time series. Our technique represents a time series using trend-value pairs on each segment belonging to a resolution level. To provide a useful representation for data mining tasks, we also propose dissimilarity measures and a symbolic representation based on the SAX technique for efficient similarity search using a multi-resolution indexing scheme. We evaluate our method for classification and discord discovery tasks over a diversity of data domains, achieving a better performance in terms of efficiency and effectiveness compared with some of the best-known classic techniques. Indeed, for some of the experiments, the time series mining algorithms using our multi-resolution representation were an order of magnitude faster, in terms of distance computations, than the state of the art.
引用
收藏
页码:75 / 96
页数:21
相关论文
共 50 条
  • [1] A Multi-resolution Approximation for Time Series
    Sanchez, Heider
    Bustos, Benjamin
    NEURAL PROCESSING LETTERS, 2020, 52 (01) : 75 - 96
  • [2] Multi-resolution Approach to Time Series Retrieval
    Fuad, Muhammad Marwan Muhammad
    Marteau, Pierre-Francois
    PROCEEDINGS OF THE FOURTEENTH INTERNATIONAL DATABASE ENGINEERING & APPLICATIONS SYMPOSIUM (IDEAS '10), 2010, : 136 - 142
  • [3] Multi-resolution Time Series Discord Discovery
    Sanchez, Heider
    Bustos, Benjamin
    ADVANCES IN COMPUTATIONAL INTELLIGENCE, IWANN 2017, PT II, 2017, 10306 : 116 - 128
  • [4] A Novel Multi-resolution Representation for Streaming Time Series
    Hu, Yupeng
    Jiang, Zifei
    Zhan, Peng
    Zhang, Qingke
    Ding, Yiming
    Li, Xueqing
    2017 INTERNATIONAL CONFERENCE ON IDENTIFICATION, INFORMATION AND KNOWLEDGE IN THE INTERNET OF THINGS, 2018, 129 : 178 - 184
  • [5] Multi-resolution Representation for Streaming Time Series Retrieval
    Luo, Wei
    Li, Yongqi
    Yao, Fubin
    Wang, Shaokun
    Li, Zhen
    Zhan, Peng
    Li, Xueqing
    INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE, 2021, 35 (06)
  • [6] Wavelet and other multi-resolution methods for time series analysis
    Scargle, JD
    STATISTICAL CHALLENGES IN MODERN ASTRONOMY II, 1997, : 333 - 347
  • [7] Respawn: A Distributed Multi-Resolution Time-Series Datastore
    Buevich, Maxim
    Wright, Anne
    Sargent, Randy
    Rowe, Anthony
    IEEE 34TH REAL-TIME SYSTEMS SYMPOSIUM (RTSS 2013), 2013, : 288 - 297
  • [8] Generation of synthetic multi-resolution time series load data
    Pinceti, Andrea
    Sankar, Lalitha
    Kosut, Oliver
    IET SMART GRID, 2023, 6 (05) : 492 - 502
  • [9] Optimized Multi-resolution Indexing and Retrieval Scheme of Time Series
    Fuad, Muhammad Marwan Muhammad
    PROGRESS IN ARTIFICIAL INTELLIGENCE-BK, 2015, 9273 : 603 - 608
  • [10] Wavelet multi-resolution approximation of time-varying frame structure
    Xiang, Min
    Xiong, Feng
    Shi, Yuanfeng
    Dai, Kaoshan
    Ding, Zhibin
    ADVANCES IN MECHANICAL ENGINEERING, 2018, 10 (08)