Emerging technologies have led to the creation of huge databases that require reducing their high dimensionality to be analysed. Many suboptimal methods have been proposed for this purpose. On the other hand, few efficient optimal methods have been proposed due to their high computational complexity. However, these methods are necessary to evaluate the performance of suboptimal methods. This paper proposes a new optimal approach, called OSTS, to improve the segmentation of time series. The proposed method is based on A* algorithm and it uses an improved version of the well-known Salotti method for obtaining optimal polygonal approximations. Firstly, a suboptimal method for time-series segmentation is applied to obtain pruning values. In this case, a suboptimal method based on Bottom-Up technique is selected. Then, the results of the suboptimal method are used as pruning values to reduce the computational time of the proposed method. The proposal has been compared to other suboptimal methods and the results have shown that the method is optimal, and, in some cases, the computational time is similar to other suboptimal methods.

新兴技术已导致创建庞大的数据库，这些数据库需要降低对其进行分析的高度维度。为此已经提出了许多次优的方法。另一方面，由于它们的高计算复杂性，很少有人提出有效的最优方法。但是，这些方法对于评估次优方法的性能是必需的。本文提出了一种新的最优方法，称为OSTS，以改善时间序列的分割。所提出的方法基于A*算法，它使用了著名的Salotti方法的改进版本来获得最佳的多边形近似值。首先，将时间序列分段的次优方法应用于获得修剪值。在这种情况下，选择基于自底向上技术的次优方法。然后，将次优方法的结果用作修剪值，以减少所提出方法的计算时间。该建议已与其他次优方法进行了比较，结果表明该方法是最佳的，并且在某些情况下，计算时间类似于其他次优方法。