We propose a simple, fast, deterministic pre-fitting approach which derives the Baum–Welch algorithm initial values directly from the input data. Such pre-fitting has the purpose of improving the fitting time for a given Hidden Markov Model (HMM) while maintaining the original Baum–Welch algorithm as the fitting one. The fitting time is improved by avoiding the Baum–Welch algorithm sensitiveness through the generation of parameters closer to the global maximum likelihood. Furthermore, by keeping the original Baum–Welch algorithm as the fitting one, we guarantee that all related methods will continue to work properly. On the other hand, the pre-fitting generates the HMM parameters directly derived from time-series data, without any data transformation, using an [Formula: see text] operation.

中文翻译：

---

HMM 拟合的分段聚合：与时间序列数据无缝集成的预拟合模型

我们提出了一种简单、快速、确定性的预拟合方法，该方法直接从输入数据中推导出 Baum-Welch 算法的初始值。这种预拟合的目的是改善给定隐马尔可夫模型 (HMM) 的拟合时间，同时保持原始 Baum-Welch 算法作为拟合算法。通过生成更接近全局最大似然的参数来避免 Baum-Welch 算法的敏感性，从而改进了拟合时间。此外，通过保留原始 Baum-Welch 算法作为拟合算法，我们保证所有相关方法将继续正常工作。另一方面，预拟合直接从时间序列数据中生成 HMM 参数，无需任何数据转换，使用 [公式：见文本] 操作。