A New Pattern Representation Method for Time-Series Data

Rezvani, Roonak; Barnaghi, Payam; Enshaeifar, Shirin

doi:10.1109/tkde.2019.2961097

Cited by 24 publications

(8 citation statements)

References 28 publications

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“…But such a procedure does not solve the pain point of video sequence analysis in a practical sense. Only using convolution operation cannot filter the information features of the image sequence and cannot meet the performance requirements of video sequence analysis [13][14][15]. erefore, researchers design an explicit processing module for the network, explicitly handling the above transformations.…”

Section: Optimization Of Lstm Network and Construction Of Internet Of...mentioning

confidence: 99%

Analysis on Time-Series Data from Movie Using MF-DCCA Method and Recurrent Neural Network Model Under the Internet of Things

Miao

Zhang

2022

Computational Intelligence and Neuroscience

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The present work aims to analyze the time-series data (TSD) from movies and support constructing the movie recommendation system. Referencing the Internet of Things (IoT) technology as the framework, a time-series data analysis system for movies is built based on the recurrent neural network (RNN) and multifractal detrended mobility cross-correlation analysis (MF-DCCA) method. First, the traditional RNN model is improved by replacing the conventional convolution operation with spatial adaptive convolution. Specifically, an additional convolution layer is used to obtain the position parameters required for adaptive convolution to improve the model performance to capture the characteristics of spatial-temporal transformation. Then, the MF-DCCA method is optimized to reduce the interference of noise signals to the analysis processing of TSD from movies. Finally, the TSD analysis system is tested for performance verification. The test results indicate that the method proposed here has outstanding stability and runs smoothly. When the prediction scheme is long short-term memory (LSTM) (L = 20), the similarity of the LSTM (L = 20) network under one frame is 0.977; the similarity of the LSTM (L = 20) network under nine frames is 0.727. This system provides a specific idea for applying the RNN model and MF-DCCA method in analyzing TSD from movies.

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Section: Optimization Of Lstm Network and Construction Of Internet Of...mentioning

confidence: 99%

Analysis on Time-Series Data from Movie Using MF-DCCA Method and Recurrent Neural Network Model Under the Internet of Things

Miao

Zhang

2022

Computational Intelligence and Neuroscience

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“…Observations are grouped in time windows of predetermined size. On each window, Lagrangian Pattern Representation (LPR) [ 62 , 63 ] is applied to determine the patterns. Patterns are then clustered and grouped using Gaussian Mixture Models (GMM).…”

Section: Enablers For Discovery and Processing Layermentioning

confidence: 99%

“…In the PE component, there are two models that represent patterns [ 63 ]. K-means clustering was used for the first approach of representing patterns and our model applied to some data sets from UCR Time-series Classification Archive [ 64 ], which is known as a benchmark data set for clustering and classification methods.…”

Section: Enablers For Discovery and Processing Layermentioning

confidence: 99%

IoTCrawler: Challenges and Solutions for Searching the Internet of Things

Iggena¹,

Ilyas²,

Fischer³

et al. 2021

Sensors

Self Cite

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Due to the rapid development of the Internet of Things (IoT) and consequently, the availability of more and more IoT data sources, mechanisms for searching and integrating IoT data sources become essential to leverage all relevant data for improving processes and services. This paper presents the IoT search framework IoTCrawler. The IoTCrawler framework is not only another IoT framework, it is a system of systems which connects existing solutions to offer interoperability and to overcome data fragmentation. In addition to its domain-independent design, IoTCrawler features a layered approach, offering solutions for crawling, indexing and searching IoT data sources, while ensuring privacy and security, adaptivity and reliability. The concept is proven by addressing a list of requirements defined for searching the IoT and an extensive evaluation. In addition, real world use cases showcase the applicability of the framework and provide examples of how it can be instantiated for new scenarios.

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“…For example, Wu and Keogh [4] focused on time series anomaly detection. Rezvani et al [5] studied a new pattern representation method for time-series data to effectively detect the change point. Sequential pattern mining method, as a commonly used method, can also be used to discover patterns of interest to users in time series [6] after discretizing the time series into symbols.…”

Section: Introductionmentioning

confidence: 99%

OPR-Miner: Order-preserving rule mining for time series

Wu¹,

Zhao²,

Li³

et al. 2022

Preprint

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Discovering frequent trends in time series is a critical task in data mining. Recently, order-preserving matching was proposed to find all occurrences of a pattern in a time series, where the pattern is a relative order (regarded as a trend) and an occurrence is a sub-time series whose relative order coincides with the pattern. Inspired by the order-preserving matching, the existing order-preserving pattern (OPP) mining algorithm employs order-preserving matching to calculate the support, which leads to low efficiency. To address this deficiency, this paper proposes an algorithm called efficient frequent OPP miner (EFO-Miner) to find all frequent OPPs. EFO-Miner is composed of four parts: a pattern fusion strategy to generate candidate patterns, a matching process for the results of sub-patterns to calculate the support of super-patterns, a screening strategy to dynamically reduce the size of prefix and suffix arrays, and a pruning strategy to further dynamically prune candidate patterns. Moreover, this paper explores the order-preserving rule (OPR) mining and proposes an algorithm called OPR-Miner to discover strong rules from all frequent OPPs using EFO-Miner. Experimental results verify that OPR-Miner gives better performance than other competitive algorithms. More importantly, clustering and classification experiments further validate that OPR-Miner achieves good performance.

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A New Pattern Representation Method for Time-Series Data

Cited by 24 publications

References 28 publications

Analysis on Time-Series Data from Movie Using MF-DCCA Method and Recurrent Neural Network Model Under the Internet of Things

Analysis on Time-Series Data from Movie Using MF-DCCA Method and Recurrent Neural Network Model Under the Internet of Things

IoTCrawler: Challenges and Solutions for Searching the Internet of Things

OPR-Miner: Order-preserving rule mining for time series

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