@inproceedings{madane_transformer-based_2023,
	title = {Transformer-based conditional generative adversarial network for multivariate time series generation},
	abstract = {Conditional generation of time-dependent data is a task that has much interest, whether for data augmentation, scenario simulation, completing missing data, or other purposes. Recent works proposed a Transformer-based Time series generative adversarial network (TTS-GAN) to address the limitations of recurrent neural networks. However, this model assumes a unimodal distribution and tries to generate samples around the expectation of the real data distribution. One of its limitations is that it may generate a random multivariate time series; it may fail to generate samples in the presence of multiple sub-components within an overall distribution. One could train models to fit each sub-component separately to overcome this limitation. Our work extends the TTS-GAN by conditioning its generated output on a particular encoded context allowing the use of one model to fit a mixture distribution with multiple sub-components. Technically, it is a conditional generative adversarial network that models realistic multivariate time series under different types of conditions, such as categorical variables or multivariate time series. We evaluate our model on UniMiB Dataset, which contains acceleration data following the XYZ axes of human activities collected using Smartphones. We use qualitative evaluations and quantitative metrics such as Principal Component Analysis (PCA), and we introduce a modified version of the Frechet inception distance (FID) to measure the performance of our model and the statistical similarities between the generated and the real data distributions. We show that this transformer-based CGAN can generate realistic high-dimensional and long data sequences under different kinds of conditions.},
	booktitle = {{PAKDD} {International} {Workshop} on {Temporal} {Analytics}},
	author = {Madane, Abdellah and Dilmi, Mohamed-djallel and Forest, Florent and Azzag, Hanane and Lebbah, Mustapha and Lacaille, Jerome},
	month = may,
	year = {2023},
	url_Link = {http://arxiv.org/abs/2210.02089},
	url_Paper = {http://arxiv.org/pdf/2210.02089.pdf},
}

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One of its limitations is that it may generate a random multivariate time series; it may fail to generate samples in the presence of multiple sub-components within an overall distribution. One could train models to fit each sub-component separately to overcome this limitation. Our work extends the TTS-GAN by conditioning its generated output on a particular encoded context allowing the use of one model to fit a mixture distribution with multiple sub-components. Technically, it is a conditional generative adversarial network that models realistic multivariate time series under different types of conditions, such as categorical variables or multivariate time series. We evaluate our model on UniMiB Dataset, which contains acceleration data following the XYZ axes of human activities collected using Smartphones. We use qualitative evaluations and quantitative metrics such as Principal Component Analysis (PCA), and we introduce a modified version of the Frechet inception distance (FID) to measure the performance of our model and the statistical similarities between the generated and the real data distributions. 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Recent works proposed a Transformer-based Time series generative adversarial network (TTS-GAN) to address the limitations of recurrent neural networks. However, this model assumes a unimodal distribution and tries to generate samples around the expectation of the real data distribution. One of its limitations is that it may generate a random multivariate time series; it may fail to generate samples in the presence of multiple sub-components within an overall distribution. One could train models to fit each sub-component separately to overcome this limitation. Our work extends the TTS-GAN by conditioning its generated output on a particular encoded context allowing the use of one model to fit a mixture distribution with multiple sub-components. Technically, it is a conditional generative adversarial network that models realistic multivariate time series under different types of conditions, such as categorical variables or multivariate time series. We evaluate our model on UniMiB Dataset, which contains acceleration data following the XYZ axes of human activities collected using Smartphones. We use qualitative evaluations and quantitative metrics such as Principal Component Analysis (PCA), and we introduce a modified version of the Frechet inception distance (FID) to measure the performance of our model and the statistical similarities between the generated and the real data distributions. We show that this transformer-based CGAN can generate realistic high-dimensional and long data sequences under different kinds of conditions.},\n\tbooktitle = {{PAKDD} {International} {Workshop} on {Temporal} {Analytics}},\n\tauthor = {Madane, Abdellah and Dilmi, Mohamed-djallel and Forest, Florent and Azzag, Hanane and Lebbah, Mustapha and Lacaille, Jerome},\n\tmonth = may,\n\tyear = {2023},\n\turl_Link = {http://arxiv.org/abs/2210.02089},\n\turl_Paper = {http://arxiv.org/pdf/2210.02089.pdf},\n}\n\n","author_short":["Madane, A.","Dilmi, M.","Forest, F.","Azzag, H.","Lebbah, M.","Lacaille, J."],"key":"madane_transformer-based_2023","id":"madane_transformer-based_2023","bibbaseid":"madane-dilmi-forest-azzag-lebbah-lacaille-transformerbasedconditionalgenerativeadversarialnetworkformultivariatetimeseriesgeneration-2023","role":"author","urls":{" link":"http://arxiv.org/abs/2210.02089"," paper":"http://arxiv.org/pdf/2210.02089.pdf"},"metadata":{"authorlinks":{}}},"bibtype":"inproceedings","biburl":"https://florentfo.rest/files/publications.bib","dataSources":["2puawT8ZAQyYRypA3"],"keywords":[],"search_terms":["transformer","based","conditional","generative","adversarial","network","multivariate","time","series","generation","madane","dilmi","forest","azzag","lebbah","lacaille"],"title":"Transformer-based conditional generative adversarial network for multivariate time series generation","year":2023}