Forecast Modelling via Variations in Binary Image-Encoded Information Exploited by Deep Learning Neural Networks.
Traditional forecasting models fit a function approximation from dependent invariables to independent variables. However, they usually get into trouble when date are presented in various formats, such as text, voice and image. This study proposes a novel image-encoded forecasting method that input and output binary digital two-dimensional (2D) images are transformed from decimal data. Omitting any data analysis or cleansing steps for simplicity, all raw variables were selected and converted to binary digital images as the input of a deep learning model, convolutional neural network (CNN). Using shared weights, pooling and multiple-layer back-propagation techniques, the CNN was adopted to locate the nexus among variations in local binary digital images. Due to the computing capability that was originally developed for binary digital bitmap manipulation, this model has significant potential for forecasting with vast volume of data. The model was validated by a power loads predicting dataset from the Global Energy Forecasting Competition 2012.
School of Economics and Management, North China Electric Power University, Beijing, China.
Da
http://orcid.org/0000-0003-0816-1901
D
Liu
School of Natural Resources and Environment, University of Michigan, Ann Arbor, MI, United States of America.
Ming
M
Xu
School of Economics and Management, North China Electric Power University, Beijing, China.
Dongxiao
D
Niu
School of Economics and Management, North China Electric Power University, Beijing, China.
Shoukai
S
Wang
School of Natural Resources and Environment, University of Michigan, Ann Arbor, MI, United States of America.
Sai
S
Liang
D000465:Algorithms; D003196:Computer Graphics; D006801:Humans; D000069550:Machine Learning; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated