Fusion of Image Transformation Techniques for IoT-based Multivariate Time-Series

The emergence of the Internet of Things (IoT) has ushered in a new era of data generation with the opportunity for data to become a key element of connected devices. This study investigates new methods to bridge the realms of multivariate time-series data and image analysis, paying special attention to Gramian Angular Summation Field (GASF), Gramian Angular Difference Field (GADF), Markov Transition Field (MTF), and Recurrence Plot (RP) transformation techniques. These techniques serve to convert raw time-series data into visual representations, laying the foundation for deeper analysis and predictive modeling. The study introduces a novel paradigm by not only employing individual image transformation techniques but also fusing them in both horizontal and square orientations. By leveraging Convolutional Neural Networks (CNNs), this study demonstrates the efficiency of innovative fused-oriented image transformation techniques in predicting complex patterns within a multivariate time-series dataset related to electricity distribution and transformer oil temperature. The experimental results indicate that the Fused-Horizontal image transformation technique, using the order RP - GADF - MTF - GASF, yields the best performance, achieving the lowest MSE of 0.01047, RMSE of 0.10235, and MAE of 0.08054. Additionally, the order RP - GADF - GASF - MTF results in the lowest MAPE of 0.21997, outperforming both Fused-Square techniques and individual methods like GASF, GADF, MTF, and RP. These findings underscore the potential of fused image transformation techniques in improving prediction accuracy, offering a significant advancement over traditional methods.