Robust Dominant Periodicity Detection for Time Series with Missing Data
This addresses the problem of accurate periodicity detection for analysts dealing with incomplete time series data, though it is incremental as it builds on existing methods with specific improvements for missing data.
The paper tackles periodicity detection in time series with block missing data by proposing a robust algorithm that includes a trend filter and autocorrelation function, proving effectiveness when missing blocks are less than 1/3 of the period length, and showing it outperforms existing methods on real-world datasets.
Periodicity detection is an important task in time series analysis, but still a challenging problem due to the diverse characteristics of time series data like abrupt trend change, outlier, noise, and especially block missing data. In this paper, we propose a robust and effective periodicity detection algorithm for time series with block missing data. We first design a robust trend filter to remove the interference of complicated trend patterns under missing data. Then, we propose a robust autocorrelation function (ACF) that can handle missing values and outliers effectively. We rigorously prove that the proposed robust ACF can still work well when the length of the missing block is less than $1/3$ of the period length. Last, by combining the time-frequency information, our algorithm can generate the period length accurately. The experimental results demonstrate that our algorithm outperforms existing periodicity detection algorithms on real-world time series datasets.