Regular Time-series Generation using SGM
This addresses the need for high-quality synthetic time-series data for applications like forecasting and classification, though it appears incremental as it adapts existing SGMs to a new domain.
The authors tackled the problem of generating realistic time-series data by applying score-based generative models (SGMs) to this domain, achieving state-of-the-art results on real-world datasets in terms of sampling diversity and quality.
Score-based generative models (SGMs) are generative models that are in the spotlight these days. Time-series frequently occurs in our daily life, e.g., stock data, climate data, and so on. Especially, time-series forecasting and classification are popular research topics in the field of machine learning. SGMs are also known for outperforming other generative models. As a result, we apply SGMs to synthesize time-series data by learning conditional score functions. We propose a conditional score network for the time-series generation domain. Furthermore, we also derive the loss function between the score matching and the denoising score matching in the time-series generation domain. Finally, we achieve state-of-the-art results on real-world datasets in terms of sampling diversity and quality.