Rebar Price Prediction Using Stacking Methods: A Comparative Analysis of LSTM and NeuralProphet Fusion Models
Year: 2025
Type: article
Abstract: The fluctuation in rebar prices directly impacts the production costs in industries such as construction and metallurgy.Accurate price prediction is crucial for informed decision-making in these sectors.Traditional time series forecasting methods, however, are insufficient in capturing the nonlinear characteristics of price fluctuations, In recent years, deep learning-based methods, such as NP(NeuralProphet) and LSTM(Long Short-Term Memory Networks), have become popular in time series forecasting due to their strong sequence modeling capabilities.This paper proposes a stacking method to create a hybrid NP-LSTM forecasting model, combining NP and LSTM models to enhance the accuracy of rebar price predictions on a monthly basis.Compared to the individual NP and LSTM models, the NP-LSTM hybrid model combines the strengths of both, compensating for their weaknesses and providing more accurate predictions.This approach enhances both model performance and its stability and reliability in real-world applications, offering significant practical value.This study demonstrates the effectiveness of the stacking method for multi-model fusion, offering a novel approach to rebar price prediction.
Source: Research Review
Author: Jingsi Hao
Institution: Hebei University of Science and Technology
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W4410211908