  Multi-Dimensional time series classification and prediction has been widely
used in many fields, such as disease prevention, fault diagnosis and action
recognition. However, the traditional method needs manual intervention and
inference, and cannot realize the figurative expression of multi-Dimensional
data, which lead to inadequate information mining. Inspired by the strong power
of deep learning technology in image processing, we propose a unified
time-series image fusion framework to transform multi-modal data into 2D-image,
and then realize automatic feature extraction and classification based on a
lightweight convolutional neural network. We present two basic image coding
methods, Gray image coding, RGB image coding, and their step coding methods.
Considering the universality of different application fields, we extended the
coding method and propose two types transform coding, Transform-RGB coding and
RGB-Transform coding, to improve the multi-domain representation ability. By
applying to three typical scenes of Parkinson's disease diagnosis, bearing
fault detection and gymnastics action recognition, we obtained the highest
classification accuracy of 100%, 92.86% and 99.70% respectively, which were all
higher than the classical processing methods. It proves the strong
classification ability and universality of our coding framework to different
multi-dimensional scenes. We expect that this method can be used and perform
well in other scenarios, and be potential to facilitate the progress of related
technology.
