Improved identification of breakpoints in piecewise regression and its applications
This work addresses a critical issue in data fitting for applications requiring reliable and interpretable models, though it appears incremental as it builds on existing greedy algorithms.
The paper tackled the problem of identifying breakpoints in piecewise regression by proposing novel greedy-based algorithms to enhance accuracy and efficiency, with computational results showing better accuracy than existing methods on real and synthetic data.
Identifying breakpoints in piecewise regression is critical in enhancing the reliability and interpretability of data fitting. In this paper, we propose novel algorithms based on the greedy algorithm to accurately and efficiently identify breakpoints in piecewise polynomial regression. The algorithm updates the breakpoints to minimize the error by exploring the neighborhood of each breakpoint. It has a fast convergence rate and stability to find optimal breakpoints. Moreover, it can determine the optimal number of breakpoints. The computational results for real and synthetic data show that its accuracy is better than any existing methods. The real-world datasets demonstrate that breakpoints through the proposed algorithm provide valuable data information.