Multi-scale fusion of multi-source data with different frequencies:A Transformer-based study on coal demand prediction

SHAO Feng; FENG Yu; SHEN Haonan; GENG Guoqiang; HUANG Peng; SHAO Hu

doi:10.13202/j.cnki.cer.2026.01.04

Abstract：Accurately predicting coal demand is crucial for ensuring national energy security,stabilizing market prices,and formulating macroeconomic policies.However,numerous factors influence coal demand,and the relevant data often originates from different departments with varied collection frequencies such as daily,ten-day,and monthly,posing significant challenges to traditional forecasting models.To address this issue,this paper proposes a deep learning model that integrates multi-frequency features—the Multi-Frequency Time-series Transformer(MFT-Former)—for coal demand prediction.The method first employs a systematic data processing pipeline to clean,align,and resample multi-source heterogeneous raw data into three time-synchronized high-,medium-,and low-frequency feature matrices.These three matrices are then used as parallel inputs and fed into a specially designed multi-input Transformer network.This network contains three independent encoder branches that capture temporal dependency patterns at their respective frequencies,and integrates the extracted deep features through a fusion layer to achieve future coal demand prediction.Using a real dataset containing multiple economic and industry indicators,this paper evaluates the model's predictive performance with the task of forecasting the next six months'demand based on data from the past twelve months.Experimental results show that the MFT-Former model can effectively integrate information from different time scales,achieving a Mean Absolute Percentage Error of 6.24%on the test set,demonstrating the method's effectiveness and accuracy in handling complex,multi-frequency time series forecasting problems.