Enhancing Multivariate Time Series Forecasting: A Novel Approach with Mallows Model Averaging and Graph Neural Networks DOI Creative Commons
Haili Zhang, Jiawei Wang,

Zhaobo Liu

et al.

Journal of Systems Science and Complexity, Journal Year: 2024, Volume and Issue: unknown

Published: July 27, 2024

Multivariate time series forecasting holds substantial practical significance, facilitates precise predictions, and informs decision-making. The complexity of nonlinear relationships the presence higher-order features in multivariate data have sparked a burgeoning interest leveraging deep learning approaches for such tasks. Existing methods often use pre-scaled neural networks, whose reliability generalization can pose challenge. In this study, authors propose an instance-wise graph-based Mallows model averaging (IGMMA) framework prediction. incorporates module into network, where extracted are utilized as inputs candidate linear models. These models combined with weights to create new layer, forming novel graph network model. Moreover, loss function is modified based on criterion, penalties imposed parameters separately. proposed method predict multicommodity futures prices, empirical results show that IGMMA has superior predictive accuracy even when small networks used. This indicates significantly reduces required training, which enables training multiple alternative large

Language: Английский

Enhancing Water Level Prediction Using Ensemble Machine Learning Models: A Comparative Analysis DOI
Saleh Alsulamy, Vijendra Kumar, Özgür Kişi

et al.

Water Resources Management, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 11, 2025

Language: Английский

Citations

5
Comparison of strategies for multistep-ahead lake water level forecasting using deep learning models DOI
Gang Li, Zhangkang Shu,

Miaoli Lin

et al.

Journal of Cleaner Production, Journal Year: 2024, Volume and Issue: 444, P. 141228 - 141228

Published: Feb. 13, 2024

Language: Английский

Citations

14
Flood Forecasting Method and Application Based on Informer Model DOI Open Access
Yiyuan Xu, Jianhui Zhao, Biao Wan

et al.

Water, Journal Year: 2024, Volume and Issue: 16(5), P. 765 - 765

Published: March 4, 2024

Flood forecasting helps anticipate floods and evacuate people, but due to the access of a large number data acquisition devices, explosive growth multidimensional increasingly demanding prediction accuracy, classical parameter models, traditional machine learning algorithms are unable meet high efficiency precision requirements tasks. In recent years, deep represented by convolutional neural networks, recurrent networks Informer models have achieved fruitful results in time series The model is used predict flood flow reservoir. At same time, compared with method LSTM model, how apply field improve accuracy studied. 28 Wan’an Reservoir control basin from May 2014 June 2020 were used, areal rainfall five subzones outflow two reservoirs as inputs processes different sequence lengths outputs. show that has good applicability forecasting. length 4, 5 6, higher better than other under length, will decline certain extent increase length. stably predicts peak better, its average difference maximum smallest. As increases, fields less 15% decreases. Therefore, can be one methods, it provides new scientific decision-making basis for reservoir control.

Language: Английский

Citations

9
Modeling the effect of meteorological drought on lake level changes with machine learning techniques DOI
Özlem Terzi, Dilek Taylan, Tahsin Baykal

et al.

Elsevier eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 227 - 246

Published: Jan. 1, 2025

Language: Английский

Citations

0
Novel methodology for prediction of missing values in River flow based on convolution neural networks: Principles and application in Iran country DOI
Saeed Farzin, Mahdi Valikhan Anaraki, Mojtaba Kadkhodazadeh

et al.

Physics and Chemistry of the Earth Parts A/B/C, Journal Year: 2025, Volume and Issue: unknown, P. 103875 - 103875

Published: Jan. 1, 2025

Language: Английский

Citations

0
Hybrid emotional neural networks and novel multi-model stacking algorithms for multi-lake water level fluctuation modeling DOI Creative Commons
Gebre Gelete, Tagesse Gichamo, Tesfalem Abraham

et al.

Earth Science Informatics, Journal Year: 2025, Volume and Issue: 18(2)

Published: Feb. 1, 2025

Language: Английский

Citations

0
Short-Term Water Level Prediction for Long-Distance Water Diversion Projects Using Data-Driven Methods with Multi-Scale Attention Mechanism DOI
Xinyong Xu,

Zhongkui Zhu,

Xiaonan Chen

et al.

Water Resources Management, Journal Year: 2025, Volume and Issue: unknown

Published: March 12, 2025

Language: Английский

Citations

0
Rationally Turbulent Expectations Chapter 5: Prediction Markets DOI

Kent Osband

SSRN Electronic Journal, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Language: Английский

Citations

0
Development of a Bayesian Network-Based Parallel Mechanism for Lower Limb Gait Rehabilitation DOI Creative Commons
H. L., Yanping Bao, Chao Jia

et al.

Biomimetics, Journal Year: 2025, Volume and Issue: 10(4), P. 230 - 230

Published: April 8, 2025

This study aims to address the clinical needs of hemiplegic and stroke patients with lower limb motor impairments, including gait abnormalities, muscle weakness, loss coordination during rehabilitation. To achieve this, it proposes an innovative design method for a rehabilitation training system based on Bayesian networks parallel mechanisms. A network model is constructed expert knowledge structural mechanics analysis, considering key factors such as scenarios, motion trajectory deviations, goals. By utilizing characteristics mechanisms, we designed device that supports multidimensional correction. three-dimensional digital developed, multi-posture ergonomic simulations are conducted. The focuses quantitatively assessing kinematic hip, knee, ankle joints while wearing device, establishing comprehensive evaluation includes range (ROM), dynamic load, optimization matching trajectories. Kinematic analysis verifies reasonable, aiding in improving patients’ gait, enhancing strength, restoring flexibility. achieves personalized goal through probability updates. mechanisms significantly expands joint motion, hip sagittal plane mobility reducing thereby validating notable effect

Language: Английский

Citations

0
Flood Classification and Improved Loss Function by Combining Deep Learning Models to Improve Water Level Prediction in a Small Mountain Watershed DOI Creative Commons

Rukai Wang,

Ximin Yuan,

Fuchang Tian

et al.

Journal of Flood Risk Management, Journal Year: 2025, Volume and Issue: 18(2)

Published: April 2, 2025

ABSTRACT Floods are major natural disasters that present considerable challenges to socioeconomic and ecological systems. Flash floods highly nonlinear exhibit rapid spatiotemporal variability. Existing methods struggle capture these features, leading suboptimal long‐term peak flood prediction accuracy. This study proposes a hierarchical model based on clustering enhance forecasting accuracy in the Heshengxi watershed. We employ STGCN GWN models with attention mechanism. Enhanced loss functions further refine Results show method is an effective means of extracting features by addressing variability parameters for different magnitudes. The integration Graph Convolutional Time Aware enables recognize characteristics, overcoming limitations prevailing ensuring forecast optimized function improves performance, resulting significant improvement prediction, reduction 0.26% relative error model. framework provides solution warning, emergency response, optimal scheduling. It also demonstrates potential deep learning field intelligent hydrological forecasting.

Language: Английский

Citations

0