A Novel Embedded Discretization-Based Deep Learning Architecture for Multivariate Time Series Classification

Tahan, Marzieh Hajizadeh; Ghasemzadeh, Mohammad; Asadi, Shahrokh

doi:10.1109/tii.2022.3188839

Cited by 5 publications

(2 citation statements)

References 29 publications

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“…In 2023, M. H. Tahan et al [1] have incorporated temporal discretization as a preprocessing step for time series data, seamlessly integrated it within DNN. These novel models were structured in two main segments: model training and temporal discretization.…”

Section: Related Workmentioning

confidence: 99%

“…Time series classification involves assigning labels or categories to sequences of data points ordered over time, and it requires specialized techniques to handle the temporal nature of the data effectively. Several traditional techniques like DNN [1] is especially valuable when dealing with complex temporal patterns and time series data often exhibit hierarchical patterns at different time scales but it can be crucial for achieving good performance in time series classification tasks. DTGNN [2] is designed to specifically handle time series data, it might offer advantages in capturing temporal dependencies and dynamics in the data, which is crucial for accurate time series classification but the adoption of a new architecture can be a limitation if it's not well-supported or integrated with existing machine learning environments.…”

Section: Research Gaps and Challengesmentioning

confidence: 99%

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A novel time series classification for multivariate data using improved deep belief-recurrent neural network with optimal dynamic time warping

Mohan Babu,

Sandhya

2024

MATEC Web Conf.

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In the past ten years, data from time series extraction has attracted a lot of attention. Several methods have concentrated on classification problems, where the objective is to identify the labelling of a test period, given labelled training data. Feature-based and Instance-based methods are the two fundamental groups into which time series categorization methodologies may be divided. To categorize time series data, instance-based techniques use similarity data in a nearest-neighbor context. While methods in this category deliver reliable findings, their efficacy suffers when dealing with lengthy and noisy time series. Feature-based approaches, on the other together, extract characteristics to address the shortcomings of instance-based methods; nevertheless, these approaches use predetermined features and might not be effective in all classification issues. This paper seeks to introduce a novel deep learning-based Optimal Dynamic Time Warping (ODTW) paradigm for multimodal time’s series data categorization. This model covers several phases. At initial stage, the standard data is gathered from standard public source. Secondly, ODTW is proposed, where the parameters are optimized by Random Opposition Billiards-Inspired Optimization (RO-BIO) for extracting the most essential information. Finally, the classification is carried out through “Deep Belief Network (DBN) and Recurrent Neural Networks (RNN) termed as Deep Belief-RNN (DB-RNN)”. Finally, the extracted deep features are given to the optimized RNN for attaining the final classified results. The simulation results have resulted in superior classification performance in terms of standard performance measures.

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Section: Related Workmentioning

confidence: 99%

Section: Research Gaps and Challengesmentioning

confidence: 99%