{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T14:33:18Z","timestamp":1773930798015,"version":"3.50.1"},"reference-count":37,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,9,13]],"date-time":"2023-09-13T00:00:00Z","timestamp":1694563200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61202479"],"award-info":[{"award-number":["61202479"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>This research introduces a novel high-accuracy time-series forecasting method, namely the Time Neural Network (TNN), which is based on a kernel filter and time attention mechanism. Taking into account the complex characteristics of time-series data, such as non-linearity, high dimensionality, and long-term dependence, the TNN model is designed and implemented. The key innovations of the TNN model lie in the incorporation of the time attention mechanism and kernel filter, allowing the model to allocate different weights to features at each time point, and extract high-level features from the time-series data, thereby improving the model\u2019s predictive accuracy. Additionally, an adaptive weight generator is integrated into the model, enabling the model to automatically adjust weights based on input features. Mainstream time-series forecasting models such as Recurrent Neural Networks (RNNs) and Long Short-Term Memory Networks (LSTM) are employed as baseline models and comprehensive comparative experiments are conducted. The results indicate that the TNN model significantly outperforms the baseline models in both long-term and short-term prediction tasks. Specifically, the RMSE, MAE, and R2 reach 0.05, 0.23, and 0.95, respectively. Remarkably, even for complex time-series data that contain a large amount of noise, the TNN model still maintains a high prediction accuracy.<\/jats:p>","DOI":"10.3390\/info14090500","type":"journal-article","created":{"date-parts":[[2023,9,13]],"date-time":"2023-09-13T05:31:28Z","timestamp":1694583088000},"page":"500","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Time-Series Neural Network: A High-Accuracy Time-Series Forecasting Method Based on Kernel Filter and Time Attention"],"prefix":"10.3390","volume":"14","author":[{"given":"Lexin","family":"Zhang","sequence":"first","affiliation":[{"name":"China Agricultural University, Beijing 100083, China"}]},{"given":"Ruihan","family":"Wang","sequence":"additional","affiliation":[{"name":"China Agricultural University, Beijing 100083, China"}]},{"given":"Zhuoyuan","family":"Li","sequence":"additional","affiliation":[{"name":"China Agricultural University, Beijing 100083, China"}]},{"given":"Jiaxun","family":"Li","sequence":"additional","affiliation":[{"name":"China Agricultural University, Beijing 100083, China"}]},{"given":"Yichen","family":"Ge","sequence":"additional","affiliation":[{"name":"China Agricultural University, Beijing 100083, China"}]},{"given":"Shiyun","family":"Wa","sequence":"additional","affiliation":[{"name":"Applied Computational Science and Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, UK"}]},{"given":"Sirui","family":"Huang","sequence":"additional","affiliation":[{"name":"China Agricultural University, Beijing 100083, China"}]},{"given":"Chunli","family":"Lv","sequence":"additional","affiliation":[{"name":"China Agricultural University, Beijing 100083, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Wang, H., Xu, R., Yang, X., Wang, Y., and Liu, Y. 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