A hybrid VMD-IGA-LSTM model for dynamic response prediction of jacket offshore platform

Ocean Engineering, Год журнала: 2025, Номер 328, С. 121110 - 121110

Опубликована: Апрель 4, 2025

Язык: Английский

A Hybrid Prediction Model for International Crude Oil Price Based on Variational Mode Decomposition with BiTCN-BiGRU-Attention Deep Learning Techniques

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Окт. 21, 2024

Predicting the price and volatility of international crude oil futures is a complex task. This paper presents novel hybrid prediction model, VMD-BiTCN-BiGRU-Attention, which integrates variational mode decomposition (VMD) advanced deep learning techniques to forecast nonlinear, non-stationary, time-varying characteristics sequences. Initially, sequence decomposed into multiple modes using VMD, enabling capture different frequency components. Each independently predicted bidirectional time convolutional network (BiTCN), captures temporal information enhances long-term dependencies through dilated convolution. Subsequently, gated recurrent unit (BiGRU) models more effectively, while an attention mechanism adjusts weights BiGRU outputs emphasize critical information. The model’s predictions are optimized with Adam algorithm. Empirical results demonstrate that model adept at forecasting non-stationary nonlinear prices. Furthermore, Diebold-Mariano (DM) test confirms this surpasses 15 other regarding accuracy performance, achieving optimal key metrics: R² = 0.9953, RMSE 1.4417, MAE 0.7973, MAPE 1.5213%. These findings underscore its potential for enhancing prediction.

Язык: Английский

Uncertainty Quantification Method for Trend Prediction of Oil Well Time Series Data Based on SDMI Loss Function

Processes, Год журнала: 2024, Номер 12(12), С. 2642 - 2642

Опубликована: Ноя. 23, 2024

IoT sensors in oilfields gather real-time data sequences from oil wells. Accurate trend predictions of these are crucial for production optimization and failure forecasting. However, well time series exhibit strong nonlinearity, requiring not only precise prediction but also the estimation uncertainty intervals. This paper first proposed a denoising method based on Variational Mode Decomposition (VMD) Long Short-Term Memory (LSTM) to reduce noise present data. Subsequently, an SDMI loss function was introduced, combining respective advantages Soft Dynamic Time Warping Mean Squared Error (MSE). The additionally accepts upper lower bounds interval as input is optimized with sequence. By predicting next 48 points, results using existing three common functions compared multiple sets. before after shown. experimental demonstrate that average coverage rate predicted intervals across seven wells 81.4%, accurately reflect trends real

Язык: Английский