IEEE Access, 2019 Aiming at the problem that the traditional similarity measurement methods cannot effectively measure the similarity of the time series with the difference both in the trend and detail, this paper proposes a new time series similarity measurement method ...
Jiancheng Yin +5 more
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Data size reduction with symbolic aggregate approximation for electrical load pattern grouping
IET Generation, Transmission & Distribution, 2013 Data size reduction techniques may be helpful in the process of categorising the electrical load consumption patterns on the basis of their shape. Starting from a macro‐class of consumers defined according to certain general criteria on the type of consumers and the period of the year or week, the representative load pattern (RLP) of each consumer can ...
NOTARISTEFANO, ANTONIO +2 more
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Symbolic Aggregate Approximation-Based Clustering of Monthly Natural Gas Consumption
Bitlis Eren Üniversitesi Fen Bilimleri DergisiNatural gas is an indispensable non-renewable energy source for many countries. It is used in many different areas such as heating and kitchen appliances in homes, and heat treatment and electricity generation in industry. Natural gas is an essential component of the transportation sector, providing a cleaner alternative to traditional fuels in ...
Mehmet Eren Nalici +2 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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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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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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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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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
core +5 more sources
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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