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时间序列中稳健的无参数季节长度检测
Robust Parameter-Free Season Length Detection in Time Series |
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| 课程网址: |
https://videolectures.net/videos/kdd2017_toller_length_detection
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| 主讲教师: |
Maximilian Toller
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| 开课单位: |
KDD 2017研讨会
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| 开课时间: |
2017-12-01
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| 课程语种: |
英语
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| 中文简介: |
近年来，时间序列的深入分析引起了人们的广泛研究兴趣，其中周期模式的识别是一个重要方面。许多识别周期模式的方法都需要时间序列的季节长度作为输入参数。自动季节长度近似的算法很少。其中许多依赖于数据离散化和用户定义参数等简化。本文提出了一种季节长度检测算法，该算法设计得足够可靠，可用于实际应用，并且不需要分析时间序列以外的任何输入。该算法通过插值、滤波和去趋势化数据来估计时间序列的季节长度。然后分析直接对应的自相关函数中零点之间的距离。我们的算法与一个类似的算法进行了测试，在165个测试中通过了122个，表现优于它，而现有的算法通过了83个测试。我们方法的稳健性可以共同归因于算法方法和在实现层面做出的设计决策。
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| 课程简介: |
The in-depth analysis of time series has gained a lot of research interest in recent years, with the identification of periodic patterns being one important aspect. Many of the methods for identifying periodic patterns require time series’ season length as input parameter. There exist only a few algorithms for automatic season length approximation. Many of these rely on simplifications such as data discretization and user defined parameters. This paper presents an algorithm for season length detection that is designed to be sufficiently reliable to be used in practical applications and does not require any input other than the time series to be analyzed. The algorithm estimates a time series’ season length by interpolating, filtering and detrending the data. This is followed by analyzing the distances between zeros in the directly corresponding autocorrelation function. Our algorithm was tested against a comparable algorithm and outperformed it by passing 122 out of 165 tests, while the existing algorithm passed 83 tests. The robustness of our method can be jointly attributed to both the algorithmic approach and also to design decisions taken at the implementational level.
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| 关 键 词: |
时间序列; 无参数季节; 长度检测
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| 课程来源: |
视频讲座网
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| 数据采集: |
2024-11-22：liyq
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| 最后编审: |
2024-11-22：liyq
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| 阅读次数: |
2
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