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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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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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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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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Methods for preprocessing time and distance series data from personal monitoring devices
MethodsX, 2020 There is a need to develop more advanced tools to improve guidance on physical exercise to reduce risk of adverse events and improve benefits of exercise.
Tomasz Wiktorski +2 more
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A Data-Driven Approach for Estimating the Effects of Station Closures in Metro Systems
IEEE Access, 2020 To estimate the impact of station closures, a novel data-driven approach is developed. First, Symbolic Aggregate Approximation (SAX) is designed to classify stations with an anomaly in passenger flow volume.
Ming Yang +4 more
doaj +1 more source
Applied Sciences, 2021 With the complexity of the task requirement, multiple operating conditions have gradually become the common scenario for equipment. However, the degradation trend of monitoring data cannot be accurately extracted in life prediction under multiple ...
Jiancheng Yin +3 more
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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
openaire +2 more sources