Visual prediction method based on time series-driven LSTM model
Significant progress has been made in time series prediction and image processing problems. However, most of the studies have focused on either the field of time series or image processing separately, failing to integrate the advantages of both fields. To overcome the limitations of existing algorit...
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| Published in: | Scientific reports Vol. 15; no. 1; pp. 38057 - 14 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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30.10.2025
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| ISSN: | 2045-2322, 2045-2322 |
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| Abstract | Significant progress has been made in time series prediction and image processing problems. However, most of the studies have focused on either the field of time series or image processing separately, failing to integrate the advantages of both fields. To overcome the limitations of existing algorithms in image temporal inference, this paper proposes a novel visual prediction framework based on the time series forecasting model, which can predict single-frame or multi-frame images by thoroughly analyzing their spatio-temporal features. Firstly, the ViT image feature extraction module is constructed by randomly masking and reconstructing the image to analyze the learned image and extract the features. Then, the time series construction module is designed to convert the extracted features into the time series model suitable for the LSTM network. Finally, the time series data is predicted based on LSTM, and the predicted time series data is transformed into the predicted image. A series of experiments is done on three types of cloud image datasets. The results are analyzed superficially and demonstrate the effectiveness and feasibility of the proposed method in terms of image prediction performance. |
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| AbstractList | Significant progress has been made in time series prediction and image processing problems. However, most of the studies have focused on either the field of time series or image processing separately, failing to integrate the advantages of both fields. To overcome the limitations of existing algorithms in image temporal inference, this paper proposes a novel visual prediction framework based on the time series forecasting model, which can predict single-frame or multi-frame images by thoroughly analyzing their spatio-temporal features. Firstly, the ViT image feature extraction module is constructed by randomly masking and reconstructing the image to analyze the learned image and extract the features. Then, the time series construction module is designed to convert the extracted features into the time series model suitable for the LSTM network. Finally, the time series data is predicted based on LSTM, and the predicted time series data is transformed into the predicted image. A series of experiments is done on three types of cloud image datasets. The results are analyzed superficially and demonstrate the effectiveness and feasibility of the proposed method in terms of image prediction performance.Significant progress has been made in time series prediction and image processing problems. However, most of the studies have focused on either the field of time series or image processing separately, failing to integrate the advantages of both fields. To overcome the limitations of existing algorithms in image temporal inference, this paper proposes a novel visual prediction framework based on the time series forecasting model, which can predict single-frame or multi-frame images by thoroughly analyzing their spatio-temporal features. Firstly, the ViT image feature extraction module is constructed by randomly masking and reconstructing the image to analyze the learned image and extract the features. Then, the time series construction module is designed to convert the extracted features into the time series model suitable for the LSTM network. Finally, the time series data is predicted based on LSTM, and the predicted time series data is transformed into the predicted image. A series of experiments is done on three types of cloud image datasets. The results are analyzed superficially and demonstrate the effectiveness and feasibility of the proposed method in terms of image prediction performance. Significant progress has been made in time series prediction and image processing problems. However, most of the studies have focused on either the field of time series or image processing separately, failing to integrate the advantages of both fields. To overcome the limitations of existing algorithms in image temporal inference, this paper proposes a novel visual prediction framework based on the time series forecasting model, which can predict single-frame or multi-frame images by thoroughly analyzing their spatio-temporal features. Firstly, the ViT image feature extraction module is constructed by randomly masking and reconstructing the image to analyze the learned image and extract the features. Then, the time series construction module is designed to convert the extracted features into the time series model suitable for the LSTM network. Finally, the time series data is predicted based on LSTM, and the predicted time series data is transformed into the predicted image. A series of experiments is done on three types of cloud image datasets. The results are analyzed superficially and demonstrate the effectiveness and feasibility of the proposed method in terms of image prediction performance. Abstract Significant progress has been made in time series prediction and image processing problems. However, most of the studies have focused on either the field of time series or image processing separately, failing to integrate the advantages of both fields. To overcome the limitations of existing algorithms in image temporal inference, this paper proposes a novel visual prediction framework based on the time series forecasting model, which can predict single-frame or multi-frame images by thoroughly analyzing their spatio-temporal features. Firstly, the ViT image feature extraction module is constructed by randomly masking and reconstructing the image to analyze the learned image and extract the features. Then, the time series construction module is designed to convert the extracted features into the time series model suitable for the LSTM network. Finally, the time series data is predicted based on LSTM, and the predicted time series data is transformed into the predicted image. A series of experiments is done on three types of cloud image datasets. The results are analyzed superficially and demonstrate the effectiveness and feasibility of the proposed method in terms of image prediction performance. |
| ArticleNumber | 38057 |
| Author | Jumahong, Huxidan Wang, Yongjie Wang, Weina Aili, Abuduwaili |
| Author_xml | – sequence: 1 givenname: Huxidan surname: Jumahong fullname: Jumahong, Huxidan organization: School of Network Security and Information Technology, YiLi Normal University, Yili Key Laboratory of Intelligent Computing Research and Application, YiLi Normal University – sequence: 2 givenname: Yongjie surname: Wang fullname: Wang, Yongjie organization: School of Science, Jilin University of Chemical Technology – sequence: 3 givenname: Abuduwaili surname: Aili fullname: Aili, Abuduwaili organization: School of Network Security and Information Technology, YiLi Normal University – sequence: 4 givenname: Weina surname: Wang fullname: Wang, Weina email: wangweina@jlict.edu.cn organization: School of Network Security and Information Technology, YiLi Normal University, School of Science, Jilin University of Chemical Technology |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/41168321$$D View this record in MEDLINE/PubMed |
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| Keywords | Visual prediction Time series forecasting Vision transformer Masked autoencoders Image processing |
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| Snippet | Significant progress has been made in time series prediction and image processing problems. However, most of the studies have focused on either the field of... Abstract Significant progress has been made in time series prediction and image processing problems. However, most of the studies have focused on either the... |
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| SubjectTerms | 639/166 639/705 Algorithms Artificial intelligence Classification Computer vision Cultural heritage Deep learning Design Efficiency Forecasting Humanities and Social Sciences Image processing Interdisciplinary subjects Masked autoencoders multidisciplinary Natural language processing Neural networks Predictions Science Science (multidisciplinary) Semantics Temporal variations Time series Time series forecasting Vision transformer Visual prediction |
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| Title | Visual prediction method based on time series-driven LSTM model |
| URI | https://link.springer.com/article/10.1038/s41598-025-21911-9 https://www.ncbi.nlm.nih.gov/pubmed/41168321 https://www.proquest.com/docview/3267278362 https://www.proquest.com/docview/3267414293 https://doaj.org/article/ee27a0f878734780b7d284e722c151af |
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