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A novel multi-resolution representation for time series sensor data analysis

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Abstract

The evolution of IoT has increased the popularity of all types of sensing devices in a variety of industrial fields and has resulted in enormous growth in the volume of sensor data. Considering the high volume and dimensionality of sensor data, the ability to perform in-depth data analysis and data mining tasks directly on the raw time series sensor data is limited. To solve this problem, we propose a novel dimensional reduction and multi-resolution representation approach for time series sensor data. This approach utilizes an appropriate number of important data points (IDPs) within a certain time series sensor data to produce a corresponding multi-resolution piecewise linear representation (MPLR), called MPLR-IDP. The results of the theoretical analyses and experiments show that MPLR-IDP can reduce the dimensionality while maintaining the important characteristics of time series data. MPLR-IDP can represent the data in a more flexible way to meet diverse needs of different users.

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Abbreviations

\(TS_n\) :

A time series with length n

PLR:

Piecewise linear representation

MPLR:

Multi-resolution PLR

BMPLR:

The basic multi-resolution PLR

EMPLR:

The extended multi-resolution PLR

PIPs:

Perceptually important points

TPs:

Turning points

IDPs:

Important data points

TSRSs:

Time series representation standards

\(Num_{seg}\) :

The user-specified number of segments

TFE :

The user-specified fitting error of entire time series

\(MFE_{seg}\) :

The user-specified maximum fitting error of segment

ARI:

Adaptive representation index

SB-Tree:

Specialized binary tree index

OBST:

The optimal binary search tree

LI:

Linear interpolation

LR:

Linear regression

\(seg{{<}} {x},{y}{>}\) :

Segment object from \(v_x\) to \(v_{y}\)

\(es_{{<} x,y{>}}\) :

The fitting error of \(seg{<} {x},{y}{>}\)

BMPLR:

Basic multi-resolution PLR

EMPLR:

Extended multi-resolution PLR

\(DS_m\) :

The time series dataset with m time series

\(MN_\mathrm{TP}\) :

The maximum number of TPs

DCR:

Data compression ratio

TSC:

Time series classification

ST:

Shapelet transformation

TDS:

Time series training dataset

SubTS :

All the time series subsequences set

FSS :

The final shapelets set

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Acknowledgements

The authors would like to thank the anonymous reviewers and the editors for their insightful comments and suggestions, which are greatly helpful for improving the quality of this paper. This work is supported by the National Natural Science Foundation of China, No.: 61772310, No.: 61702300, No.: 61702302, No.: 61802231; the Science and Technology Development Funds of Shandong Province, No.: 2014GGX101028; the Project of Qingdao Postdoctoral Applied Research.

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Authors and Affiliations

  1. School of Computer Science and Technology, Shandong University, Tsingtao, 266000, China

    Yupeng Hu

  2. School of Information Science and Engineering, Shandong Normal University, Jinan, 250358, China

    Cun Ji & Qingke Zhang

  3. School of Software, Shandong University, Jinan, 250101, China

    Lin Chen, Peng Zhan & Xueqing Li

Authors
  1. Yupeng Hu
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  2. Cun Ji
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  3. Qingke Zhang
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  4. Lin Chen
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  5. Peng Zhan
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  6. Xueqing Li
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Correspondence to Xueqing Li.

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Hu, Y., Ji, C., Zhang, Q. et al. A novel multi-resolution representation for time series sensor data analysis. Soft Comput 24, 10535–10560 (2020). https://doi.org/10.1007/s00500-019-04562-7

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