Peak detection is a problem in sequential data analysis that involves differentiating regions with higher counts (peaks) from regions with lower counts (background noise). It is crucial to correctly predict areas that deviate from the background noise, in both the train and test sets of labels. Dynamic programming changepoint algorithms have been proposed to solve the peak detection problem by constraining the mean to alternatively increase and then decrease. The current constrained changepoint algorithms only create predictions on the test set, while completely ignoring the train set. Changepoint algorithms that are both accurate when fitting the train set, and make predictions on the test set, have been proposed but not in the context of peak detection models. We propose to resolve these issues by creating a new dynamic programming algorithm, FLOPART, that has zero train label errors, and is able to provide highly accurate predictions on the test set. We provide an empirical analysis that shows FLOPART has a similar time complexity while being more accurate than the existing algorithms in terms of train and test label errors.
翻译:峰值探测是连续数据分析中的一个问题,它涉及将高计数(峰值)区域与低计数(低计数区域)区域(低计数区域)区分开来(地下噪音),正确预测在火车和测试标签组中不同于背景噪音的地区至关重要。 动态编程变化点算法已经提出,通过限制替代增加和随后减少的平均值来解决峰值探测问题。 目前受限制的变化点算法只对测试集作出预测,而完全忽略火车集。 已经提出了既准确适应火车组和对测试组作出预测的改变点算法,但对于峰值探测模型来说都是准确的。 我们提议通过创建新的动态编程算法(FLOPART)来解决这些问题,该算法含有零列车标签错误,并且能够对测试集提供非常准确的预测。 我们提供实验性分析,显示FLOPART具有相似的时间复杂性,同时在火车和测试标签错误方面比现有的算法更准确。