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

Imran Bamus; Feyza Yıldırım Okay; Abdullah Enes Gün; Sedef Demirci

Makale detayları

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

Yazarlar:

Imran Bamus | Feyza Yıldırım Okay | Abdullah Enes Gün | Sedef Demirci

Yayıncı:

Gazi University

Ana Başlıklar:

Deep Learning | Neural Networks | Knowledge Representation and Reasoning

Dergi:

Gazi University Journal of Science Vol.38 Issue 1, pp.115-129

Belge Tipi:

Makale

Yayın Tarihi:

1/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001341137

pISSN:

2147-1762

Anahtar kelimeler:

Özet:

 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.

 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.

 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.

 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.

Makale detayları

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

Yazarlar:

Imran Bamus | Feyza Yıldırım Okay | Abdullah Enes Gün | Sedef Demirci

Yayıncı:

Gazi University

Ana Başlıklar:

Deep Learning | Neural Networks | Knowledge Representation and Reasoning

Dergi:

Gazi University Journal of Science Vol.38 Issue 1, pp.115-129

Belge Tipi:

Makale

Yayın Tarihi:

1/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001341137

pISSN:

2147-1762

IoT

Time-series data

Image transformation

Fusion

Deep learning

Sayı

Makale detayları

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

Yazarlar: Imran Bamus | Feyza Yıldırım Okay | Abdullah Enes Gün | Sedef Demirci

Yayıncı: Gazi University

Ana Başlıklar: Deep Learning | Neural Networks | Knowledge Representation and Reasoning

Dergi: Gazi University Journal of Science Vol.38 Issue 1, pp.115-129

Belge Tipi: Makale

Yayın Tarihi: 1/2025

Belge Dili: İngilizce

Document ID (Org No.): 2001341137

pISSN: 2147-1762

IoT

Time-series data

Image transformation

Fusion

Deep learning

Sayı

Yazarlar:

Imran Bamus | Feyza Yıldırım Okay | Abdullah Enes Gün | Sedef Demirci

Yayıncı:

Gazi University

Ana Başlıklar:

Deep Learning | Neural Networks | Knowledge Representation and Reasoning

Dergi:

Gazi University Journal of Science Vol.38 Issue 1, pp.115-129

Belge Tipi:

Makale

Yayın Tarihi:

1/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001341137

pISSN:

2147-1762