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.
@article{arxiv.2108.10155,
title = {Construction Cost Index Forecasting: A Multi-feature Fusion Approach},
author = {Tianxiang Zhan and Yuanpeng He and Fuyuan Xiao},
journal= {arXiv preprint arXiv:2108.10155},
year = {2022}
}