English(EN) A Granular Framework for Construction Material Price Forecasting: Econometric and Machine-Learning Approaches

新框架使用LSTM进行精细化建筑材料价格预测

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-05 05:00

一篇新研究论文介绍了一个精细化的建筑材料价格预测框架，该框架利用了建筑规范协会（CSI）的MasterFormat结构。该框架整合了商品指数和宏观经济指标等解释变量，以提高预测准确性。在评估的模型中，长短期记忆（LSTM）网络表现出最高的性能，与传统统计方法相比，显著降低了误差。 AI

影响通过提供更准确的材料价格预测，该框架可以改进建筑行业的预算和成本估算。

排序理由学术论文，详细介绍了新框架和模型评估。[lever_c_demoted from research: ic=1 ai=0.7]

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Signal score: 19 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 学术论文，详细介绍了新框架和模型评估。[lever_c_demoted from research: ic=1 ai=0.7]
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arXiv cs.LG TIER_1 English(EN) · Boge Lyu, Qianye Yin, Iris Denise Tommelein, Hanyang Liu, Karnamohit Ranka, Karthik Yeluripati, Junzhe Shi · 2026-11-05 05:00

用于建筑材料价格预测的精细化框架：计量经济学与机器学习方法

arXiv:2512.09360v2 Announce Type: replace Abstract: The persistent volatility of construction material prices poses significant risks to cost estimation, budgeting, and project delivery, underscoring the urgent need for granular and scalable forecasting methods. This study develo…