Construction Cost Index Forecasting: A Multi-feature Fusion Approach

TL;DR

This paper introduces a multi-feature fusion neural network model that combines information fusion and machine learning to improve the accuracy and efficiency of construction cost index forecasting.

Contribution

It proposes a novel multi-feature fusion module for time series prediction, enhancing prediction accuracy over traditional convolution-based methods.

Findings

MFF module improves CCI prediction accuracy

MFF combined with MLP outperforms other models

Model demonstrates high prediction efficiency

Abstract

The construction cost index is an important indicator of the construction industry. Predicting CCI has important practical significance. This paper combines information fusion with machine learning, and proposes a multi-feature fusion (MFF) module for time series forecasting. The main contribution of MFF is to improve the prediction accuracy of CCI, and propose a feature fusion framework for time series. Compared with the convolution module, the MFF module is a module that extracts certain features. Experiments have proved that the combination of MFF module and multi-layer perceptron has a relatively good prediction effect. The MFF neural network model has high prediction accuracy and prediction efficiency, which is a study of continuous attention.