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New framework uses LSTM for granular construction material price forecasting

By PulseAugur Editorial · [1 sources] ·

A new research paper introduces a granular framework for forecasting construction material prices, utilizing the Construction Specifications Institute (CSI) MasterFormat structure. The framework integrates explanatory variables like commodity indexes and macroeconomic indicators to improve predictive accuracy. Among the evaluated models, a long short-term memory (LSTM) network demonstrated the highest performance, achieving significant error reductions compared to traditional statistical methods. AI

IMPACT This framework could improve budgeting and cost estimation in the construction industry by providing more accurate material price forecasts.

RANK_REASON Academic paper detailing a new framework and model evaluation. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New framework uses LSTM for granular construction material price forecasting

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Academic paper detailing a new framework and model evaluation. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Boge Lyu, Qianye Yin, Iris Denise Tommelein, Hanyang Liu, Karnamohit Ranka, Karthik Yeluripati, Junzhe Shi ·

    A Granular Framework for Construction Material Price Forecasting: Econometric and Machine-Learning Approaches

    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…

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