Representation and analysis of time-series data via deep embedding and visual exploration

Zhou, Yixuan; Jiang, Runfeng; Qin, Haiyang; Hu, Haibo

doi:10.1007/s12650-022-00890-3

Cited by 4 publications

(4 citation statements)

References 39 publications

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“…According to the respective results and focusing on largest pattern sizes (i.e., sets of co‐occurring categories), we can describe the following profile of a VA technique involving embeddings: designed for the AI/ML domain, using neural network approaches for embedding computations (while not specifying explicitly the size of respective embedding vectors), supporting the visual analytic task of comparison/selection, explicit visual representation of embeddings (or derived results) and interaction with them, while discussing evaluation of such human‐centered aspects in the respective publication. This pattern is supported by 9 entries (7% of the complete data set) in our current survey data [FZCM20, GHM21, SKB*21, CZG*22,PdSP*22,RSL*22,ZJQH22,HHS*23,WHC*23]. Shorter patterns occur more frequently, up to 15 entries (12%) supporting the pattern of the AI/ML domain, text data, neural networks, and interactive visualization & evaluation concerns .…”

Section: Survey Data Analysessupporting

confidence: 69%

“…We classify the computational approach into three main methods, which can of course be used in combination within the scope of a single paper. The most common, with 88 occurrences, is neu ral network, where different types of ML/DL models are used to obtain the embeddings [ZJQH22,RSL*22, SDXR22]. Even though there are many different variants, some of which are highly customized for the specific tasks, our analysis shows that using the BERT [DCLT19] model or word2vec [MSC*13] (or some of the other versions inspired/derived from it) is a common choice among the surveyed papers.…”

Section: Categorization Of Va + Embedding Approachesmentioning

confidence: 99%

“…For instance, there are examples of analysis of sequential data (time series, mobility patterns, etc.) [LPH*20,BZQ*21,ZJQH22] as well as of applications that load embedding vectors that have been calculated and published by other parties [HKMG22, EHA*22]. Graph/network data also accounts for an important contribution.…”

Section: Categorization Of Va + Embedding Approachesmentioning

confidence: 99%

See 2 more Smart Citations

VA + Embeddings STAR: A State‐of‐the‐Art Report on the Use of Embeddings in Visual Analytics

Huang

Witschard

Kucher

et al. 2023

Computer Graphics Forum

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Over the past years, an increasing number of publications in information visualization, especially within the field of visual analytics, have mentioned the term “embedding” when describing the computational approach. Within this context, embeddings are usually (relatively) low‐dimensional, distributed representations of various data types (such as texts or graphs), and since they have proven to be extremely useful for a variety of data analysis tasks across various disciplines and fields, they have become widely used. Existing visualization approaches aim to either support exploration and interpretation of the embedding space through visual representation and interaction, or aim to use embeddings as part of the computational pipeline for addressing downstream analytical tasks. To the best of our knowledge, this is the first survey that takes a detailed look at embedding methods through the lens of visual analytics, and the purpose of our survey article is to provide a systematic overview of the state of the art within the emerging field of embedding visualization. We design a categorization scheme for our approach, analyze the current research frontier based on peer‐reviewed publications, and discuss existing trends, challenges, and potential research directions for using embeddings in the context of visual analytics. Furthermore, we provide an interactive survey browser for the collected and categorized survey data, which currently includes 122 entries that appeared between 2007 and 2023.

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Section: Survey Data Analysessupporting

confidence: 69%

Section: Categorization Of Va + Embedding Approachesmentioning

confidence: 99%

Section: Categorization Of Va + Embedding Approachesmentioning

confidence: 99%

See 1 more Smart Citation

VA + Embeddings STAR: A State‐of‐the‐Art Report on the Use of Embeddings in Visual Analytics

Huang

Witschard

Kucher

et al. 2023

Computer Graphics Forum

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show abstract

“…The representation of time-series data is crucial for several analytical tasks, including comparison analysis, clustering, and classification. Conventional illustration methods yield uniform representations derived from statistical data [1]. Moreover, analyzing time-series data on currency values such as bitcoin can provide insights for investment decisions.…”

Section: Introductionmentioning

confidence: 99%

Wide-TSNet: A Novel Hybrid Approach for Bitcoin Price Movement Classification

Yamak,

Li,

Zhang

et al. 2024

Applied Sciences

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In this paper, we introduce Wide-TSNet, a novel hybrid approach for predicting Bitcoin prices using time-series data transformed into images. The method involves converting time-series data into Markov transition fields (MTFs), enhancing them using histogram equalization, and classifying them using Wide ResNets, a type of convolutional neural network (CNN). We propose a tripartite classification system to accurately represent Bitcoin price trends. In addition, we demonstrate the effectiveness of Wide-TSNet through various experiments, in which it achieves an Accuracy of approximately 94% and an F1 score of 90%. It is also shown that lightweight CNN models, such as SqueezeNet and EfficientNet, can be as effective as complex models under certain conditions. Furthermore, we investigate the efficacy of other image transformation methods, such as Gramian angular fields, in capturing the trends and volatility of Bitcoin prices and revealing patterns that are not visible in the raw data. Moreover, we assess the effect of image resolution on model performance, emphasizing the importance of this factor in image-based time-series classification. Our findings explore the intersection between finance, image processing, and deep learning, providing a robust methodology for financial time-series classification.

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Exploring the Efficiency of Clustered Neural Network Aggregators for Time Series

Sivaraman,

Tembhre,

Mishra

et al. 2024

Lecture Notes in Electrical Engineering

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Representation and analysis of time-series data via deep embedding and visual exploration

Cited by 4 publications

References 39 publications

VA + Embeddings STAR: A State‐of‐the‐Art Report on the Use of Embeddings in Visual Analytics

VA + Embeddings STAR: A State‐of‐the‐Art Report on the Use of Embeddings in Visual Analytics

Wide-TSNet: A Novel Hybrid Approach for Bitcoin Price Movement Classification

Exploring the Efficiency of Clustered Neural Network Aggregators for Time Series

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