文献类型：期刊文献

英文题名：Effective Short-Term Continuous Data Prediction Using LSTM-NHITS

作者：Peng, Xiaohong[1];Zhong, Tianrong[1];Yang, Renyou[2];Li, Zhao[1]

机构：[1]Guangdong Ocean Univ, Sch Math & Comp Sci, Zhanjiang, Peoples R China;[2]Southern Marine Sci & Engn Guangdong Lab Zhanjiang, Zhanjiang, Peoples R China

年份：2026

卷号：27

期号：3

起止页码：413

外文期刊名：JOURNAL OF INTERNET TECHNOLOGY

收录：SCI-EXPANDED(收录号：WOS:001784948000011)、、EI(收录号：20262420878190)、Scopus(收录号：2-s2.0-105041235089)、WOS

基金：This work was partially supported by the Fund of Southern Marine Science and Engineering Guangdong Laboratory (Zhanjiang) (No. ZJW-2023-04); the National Key Research and Development Program of China (No. 2022YFD2401304); the Scientific Research Project of the Education Department of Guangdong Province (No. 2024ZDZX4060, 2022GCZX001, 2023ZDZX4012); Special Fund Project for Talent Development Strategy of Guangdong Province (No. 2024R1003); Zhanjiang Science and Technology Plan Project (Grant No. 2024B01067); and the program for scientific research start-up funds of Guangdong Ocean University (Grant No. 060302102302).

语种：英文

外文关键词：level attention mechanism; LSTM-NHITS; Short-term; prediction; Zhanjiang City

外文摘要：Accurate short-term continuous data prediction is crucial for timely water quality assessment and pollution prevention. However, the nonlinear and temporally dependent nature of water quality data presents significant challenges for traditional forecasting models. To address these challenges, we propose an effective short-term continuous prediction model, LSTM-NHITS, which combines Long Short-Term Memory (LSTM) networks with Neural Hierarchical Interpolation for Time Series (NHITS). This model effectively captures multi-scale features and complex temporal dependencies, improving prediction accuracy. Experimental results from datasets collected at multiple monitoring stations in Zhanjiang City show that LSTM-NHITS outperforms traditional models across different short-term forecasting horizons (4-hour, 12-hour, and 1-day). By accurately modeling both long-and short-term dependencies, this approach ensures precise continuous water quality prediction, demonstrating its potential for real-time environmental monitoring and management.