High-quality and controllable time series generation with diffusion in transformers

Hongyu Sun, Min Hua

Keywords: Diffusion in Transformers, time series generation

Abstract: Current research on time series generation frequently depends on oversimplified data and lenient evaluation methods, making it challenging to apply these models effectively in real-world scenarios. Diffusion in Transformers (DiT) has demonstrated that the traditional inductive biases in neural networks are unnecessary. This paper shows that the advantages of DiT can be extended to time series generation. We add the attention mask and dilated causal convolution to introduce the temporal characteristic. Additionally, we introduce a novel smooth guidance policy for style control during generation, leveraging a property of the diffusion process. Furthermore, our proposed model can generate longer sequences with training in short sequences. Experimental results reveal that our variant of DiT achieves state-of-the-art performance across various data types.

Primary Area: generative models

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Submission Number: 1629