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@@ -18,7 +18,7 @@ Proceedings of the 39th Conference on Neural Information Processing Systems (Neu
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## Motivation
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Imagine you are a quantitative researcher who wants to stress-test trading strategies. You would want access to a tool that can precisely generate high-fidelity stock price time series for prompts like
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Generate Tesla’s stock price for the next month with 5% volatility.
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**Generate Tesla’s stock price for the next month with 5% volatility.**
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Additionally, for the stocks domain, there are some inherent rules that a generated sample should adhere to, such as the opening and closing prices for a day should be bounded by the highest and lowest prices for that day. This problem exists in almost all engineering domains, where certain domain-specific constraints may arise due to the laws of physics or the underlying process. We refer to this as the **constrained time series generation problem**.
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