Open AccessForecast Modelling via Variations in Binary Image-Encoded Information Exploited by Deep Learning Neural Networks
Author(s) -
Da Li,
Min Xu,
Dongxiao Niu,
Shoukai Wang,
Sai Liang
Publication year - 2016
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0157028
Subject(s) - computer science , convolutional neural network , artificial intelligence , pooling , deep learning , artificial neural network , bitmap , binary number , binary data , pattern recognition (psychology) , data mining , digital imaging , image (mathematics) , digital image , machine learning , image processing , mathematics , arithmetic
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.