Hexadecimal Aggregate Approximation Representation and Classification of Time Series Data
Algorithms, 2021 Time series data are widely found in finance, health, environmental, social, mobile and other fields. A large amount of time series data has been produced due to the general use of smartphones, various sensors, RFID and other internet devices. How a time
Zhenwen He +3 more
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A Symbolic Time-series Data Mining Framework for Analyzing Load Profiles of Electricity Consumption [PDF]
Journal of Library and Information Studies, 2017 Electricity is critical for industrial and economic advancement, as well as a driving force for sustainable development. In turn, reducing energy consumption for sustainability and both tracking and managing energy efficiently have become critical ...
I-Chin Wu +4 more
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A Boundary Distance-Based Symbolic Aggregate Approximation Method for Time Series Data
Algorithms, 2020 A large amount of time series data is being generated every day in a wide range of sensor application domains. The symbolic aggregate approximation (SAX) is a well-known time series representation method, which has a lower bound to Euclidean distance and
Zhenwen He +3 more
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An Approach of Electrical Load Profile Analysis Based on Time Series Data Mining
IEEE Access, 2020 In the current electrical load profile analysis, considering the shortage of traditional methods on the typical load profile extraction of single consumers and the load profile feature extraction, this paper proposes an approach based on time series data
Ying Shi +5 more
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SAX-STGCN: Dynamic Spatio-Temporal Graph Convolutional Networks for Traffic Flow Prediction
IEEE Access, 2022 Accurate, timely, and reliable traffic flow prediction is essential for an intelligent transportation system due to the complex spatio-temporal correlation of traffic flow.
Bin Lei, Peng Zhang, Yifei Suo, Na Li
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Approximately bisimilar symbolic models for nonlinear control systems [PDF]
, 2007 Control systems are usually modeled by differential equations describing how physical phenomena can be influenced by certain control parameters or inputs.
Girard, Antoine +2 more
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Similarity Measurement and Classification of Temporal Data Based on Double Mean Representation
Algorithms, 2023 Time series data typically exhibit high dimensionality and complexity, necessitating the use of specific approximation methods to perform computations on the data.
Zhenwen He, Chi Zhang, Yunhui Cheng
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Characterizations of Temporal Postoperative Pain Signatures With Symbolic Aggregate Approximations [PDF]
The Clinical Journal of Pain, 2017 Objectives: The primary aim was to characterize the temporal dynamics of postoperative pain intensity using symbolic aggregate approximation (SAX). The secondary aim was to explore the effects of sociodemographic and clinical factors on the SAX representations of postoperative pain intensity ...
Patrick J, Tighe +3 more
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Tri-Partition Alphabet-Based State Prediction for Multivariate Time-Series
Applied Sciences, 2021 Recently, predicting multivariate time-series (MTS) has attracted much attention to obtain richer semantics with similar or better performances. In this paper, we propose a tri-partition alphabet-based state (tri-state) prediction method for symbolic ...
Zuo-Cheng Wen +6 more
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Distributional Representation of Cyclic Alternating Patterns for A-Phase Classification in Sleep EEG
Applied Sciences, 2023 This article describes a detailed methodology for the A-phase classification of the cyclic alternating patterns (CAPs) present in sleep electroencephalography (EEG).
Diana Laura Vergara-Sánchez +2 more
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