A New Time Series Representation Model and Corresponding Similarity Measure for Fast and Accurate Similarity Detection

A time series representation model for accurate and fast similarity detection

Pattern Recognition ◽

10.1016/j.patcog.2009.03.030 ◽

2009 ◽

Vol 42 (11) ◽

pp. 2998-3014 ◽

Cited By ~ 39

Author(s):

Francesco Gullo ◽

Giovanni Ponti ◽

Andrea Tagarelli ◽

Sergio Greco

Keyword(s):

Time Series ◽

Series Representation ◽

Similarity Detection ◽

Representation Model

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Edge4TSC: Binary Distribution Tree-Enabled Time Series Classification in Edge Environment

Sensors ◽

10.3390/s20071908 ◽

2020 ◽

Vol 20 (7) ◽

pp. 1908

Author(s):

Chao Ma ◽

Xiaochuan Shi ◽

Wei Li ◽

Weiping Zhu

Keyword(s):

Time Series ◽

Deep Learning ◽

Classification Accuracy ◽

Time Series Data ◽

Series Representation ◽

Series Data ◽

Feature Engineering ◽

Time Series Classification ◽

Binary Distribution ◽

New Time

In the past decade, time series data have been generated from various fields at a rapid speed, which offers a huge opportunity for mining valuable knowledge. As a typical task of time series mining, Time Series Classification (TSC) has attracted lots of attention from both researchers and domain experts due to its broad applications ranging from human activity recognition to smart city governance. Specifically, there is an increasing requirement for performing classification tasks on diverse types of time series data in a timely manner without costly hand-crafting feature engineering. Therefore, in this paper, we propose a framework named Edge4TSC that allows time series to be processed in the edge environment, so that the classification results can be instantly returned to the end-users. Meanwhile, to get rid of the costly hand-crafting feature engineering process, deep learning techniques are applied for automatic feature extraction, which shows competitive or even superior performance compared to state-of-the-art TSC solutions. However, because time series presents complex patterns, even deep learning models are not capable of achieving satisfactory classification accuracy, which motivated us to explore new time series representation methods to help classifiers further improve the classification accuracy. In the proposed framework Edge4TSC, by building the binary distribution tree, a new time series representation method was designed for addressing the classification accuracy concern in TSC tasks. By conducting comprehensive experiments on six challenging time series datasets in the edge environment, the potential of the proposed framework for its generalization ability and classification accuracy improvement is firmly validated with a number of helpful insights.

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A Data Representation Model for Personalized Medicine

International Journal of Healthcare Information Systems and Informatics ◽

10.4018/ijhisi.295822 ◽

2021 ◽

Vol 16 (4) ◽

pp. 0-0

Keyword(s):

Time Series ◽

Personalized Medicine ◽

Series Representation ◽

Data Representation ◽

Two Dimensions ◽

Approximation Technique ◽

Data Types ◽

Nominal Data ◽

Representation Model ◽

Proposed Model

Personalized medicine exploits the patient data, for example, genetic compositions, and key biomarkers. During the data mining process, the key challenges are the information loss, the data types heterogeneity and the time series representation. In this paper, a novel data representation model for personalized medicine is proposed in light of these challenges. The proposed model will account for the structured, temporal and non-temporal data and their types, namely, numeric, nominal, date, and Boolean. After the "Date and Boolean" data transformation, the nominal data are treated by dispersion while several clustering techniques are deployed to control the numeric data distribution. Ultimately, the transformation process results in three homogeneous representations with these representations having only two dimensions to ease the exploration of the represented dataset. Compared to the Symbolic Aggregate Approximation technique, the proposed model preserves the time-series information, conserves as much data as possible and offers multiple simple representations to be explored.

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