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arXiv Machine Learning By Subo Wei, Jianqi Gao, Mingyan Fan, Shaorong Xie, Xinzhi Wang, Yongpeng Dong

ShapeLex: Decoupling Local Shape Symbolization and Global Scale Modeling for Text-Controlled Time Series Generation

Read the original on arXiv Machine Learning →

ShapeLex introduces a two-stage approach for text-controlled time series generation. It first creates a reusable vocabulary of discrete shape units—such as rises, spikes, and sharp drops—derived from training data, then uses an autoregressive generator to select and arrange these shapes based on textual input while adjusting their position and duration. Finally, a mixture-density scale head models global attributes like level and volatility to produce realistic time series that align closely with real data distributions.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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