A Gradient-Sampling-based Algorithm for Change Point Detection in Piecewise Linear Model

Kai Xiao, Yimin Shen, Xiaorui Qian, Xiangpeng Zhan, Yuanyuan Guo, Wen Huang
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Abstract

Change point detection, as an important technique in artificial intelligence, aims to identify abrupt changes in complex systems. In this paper, we propose a novel gradient-sampling-based approach for change point detection in piecewise linear model. The convergence to a point satisfying the first-order optimality condition is guaranteed. Through extensive numerical experiments, we compare the proposed algorithm with the well known method of Muggeo's segmentation by dynamic programming. By computing the change points on the dataset concerning the relationship between the residential electricity consumption and temperature in Fujian Province, we demonstrate that the proposed algorithm outperforms Muggeo's method. Moreover, when using the change points for power load forecasting, the change points from the proposed algorithm can significantly improve the predictive performance of the Long Short-Term Memory (LSTM) model.
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分段线性模型中基于梯度采样的变化点检测算法
变化点检测是人工智能领域的一项重要技术,其目的是识别复杂系统中的突变。本文提出了一种基于梯度采样的分段线性模型变化点检测方法。保证了算法收敛到满足一阶最优性条件的一点。通过大量的数值实验,将该算法与常用的Muggeo动态规划分割方法进行了比较。通过计算福建省居民用电量与温度关系数据集上的变化点,我们证明了该算法优于Muggeo方法。此外,当使用变化点进行电力负荷预测时,该算法的变化点可以显著提高长短期记忆(LSTM)模型的预测性能。
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