Construction Cost Index Forecasting: A Multi-feature Fusion Approach

Abstract

The construction cost index is an important indicator in the construction industry. Predicting CCI has great practical significance. This paper combines information fusion with machine learning, and proposes a Multifeature Fusion framework for time series forecasting. MFF uses a sliding window algorithm and proposes a function sequence to convert the time sequence into a feature sequence for information fusion. MFF replaces the traditional information method with machine learning to achieve information fusion, which greatly improves the CCI prediction effect. MFF is of great significance to CCI and time series forecasting.

İnşaat Maliyet Endeksi Tahmininde Holt-Winters Üstel Düzeltme Parametrelerinin PSO ve İleri Walk-Forward Cross-Validation ile Optimizasyonu

“…These challenges have the potential to impact the accuracy of cost predictions (Choi et al, 2021). The CCI might not fully reflect local variations in construction costs, necessitating adjustments to factor in specific regional circumstances (Zhan et al, 2021). Additionally, the CCI may not comprehensively consider the influence of macroeconomic elements on construction expenses, prompting the exploration of other economic indicators to enhance cost predictions (Fachrurrazi, 2016).…”