A New Approach for Testing Periodicity

Almasri, Abdullah

doi:10.1080/03610920903564743

Cited by 6 publications

(3 citation statements)

References 12 publications

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“…Unfortunately, it only works on discrete event sequences. Some other types of periodicity detection algorithms in the literature include wavelet transform based method [1], dynamic time warping based method [6], epoch folding [8], etc.…”

Section: Related Workmentioning

confidence: 99%

“…5.1.1 Existing Algorithms. We compare RobustPeriod with six algorithms, including three single-periodicity detection algorithm: 1) findFrequency [11]; 2) SAZED maj ; and 3) SAZED opt [26], and three multi-periodicities detection algorithms: 4) Siegel [15,24]; 5) AUTOPERIOD [18,28]; and 6) Wavelet-Fisher [1]. As the trend component may bias the periodicity detection results significantly, we apply H-P filter to remove the trend component for all algorithms for a fair comparison in our experiments.…”

Section: Baseline Algorithms and Datasetsmentioning

confidence: 99%

“…Periodicity detection has a long history in a variety of different fields, including data mining [28], statistics [1], astronomy [8], bioinformatics [32], etc. Among various methods proposed for periodicity detection, two most popular types of methods are: 1) frequency domain methods identifying the underlying periodic patterns by transforming time series into the frequency domain; 2) time domain methods correlating the signal with itself via autocorrelation function (ACF).…”

Section: Introductionmentioning

confidence: 99%

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RobustPeriod: Time-Frequency Mining for Robust Multiple Periodicity Detection

Wen,

He,

Sun

et al. 2020

Preprint

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Periodicity detection is an important task in time series analysis as it plays a crucial role in many time series tasks such as classification, clustering, compression, anomaly detection, and forecasting. It is challenging due to the following reasons: 1) complicated nonstationary time series; 2) dynamic and complicated periodic patterns, including multiple interlaced periodic components; 3) outliers and noises. In this paper, we propose a robust periodicity detection algorithm to address these challenges. Our algorithm applies maximal overlap discrete wavelet transform to transform the time series into multiple temporal-frequency scales such that different periodicities can be isolated. We rank them by wavelet variance and then at each scale, and then propose Huber-periodogram by formulating the periodogram as the solution to M-estimator for introducing robustness. We rigorously prove the theoretical properties of Huber-periodogram and justify the use of FisherâĂŹs test on Huber-periodogram for periodicity detection. To further refine the detected periods, we compute unbiased autocorrelation function based on Wiener-Khinchin theorem from Huber-periodogram for improved robustness and efficiency. Experiments on synthetic and real-world datasets show that our algorithm outperforms other popular ones for both single and multiple periodicity detection. It is now implemented and provided as a public online service at Alibaba Group and has been used extensive in different business lines. CCS CONCEPTS• Mathematics of computing → Time series analysis; • Information system → Data mining.

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Section: Related Workmentioning

confidence: 99%

Section: Baseline Algorithms and Datasetsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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RobustPeriod: Time-Frequency Mining for Robust Multiple Periodicity Detection

Wen,

He,

Sun

et al. 2020

Preprint

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A tail-tolerant cloud storage scheduling based on precise periodicity detection

Han

et al. 2022

CCF Trans. HPC

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Cloud storage is a fundamental component of the cloud computing system, which significantly affects the overall performance and quality of service of the cloud. Cloud storage servers face the challenge of imbalanced workloads. According to our observations on the time series generated by cloud storage, we found that the imbalance workloads will dramatically increase the tail latency of data access in the multi-tenant scenario. The intuitive solution is to periodicity detect the imbalance storage nodes and re-balance the loads. However, there are four challenges to accurately detect load of storage in the cloud with multiple tenants since the load may change frequently in cloud. This paper proposes PrecisePeriod, a precise periodicity detection algorithm customized for multi-tenant cloud storage. It removes outliers through data preprocessing, employs the discrete wavelet transform to remove high-frequency noise while keeping frequency domain information, computes the candidate periodicity queue using the autocorrelation function, and determines precise period through periodicity verification. Then, we design a cloud storage load balancing scheduling strategy based on PrecisePeriod, and the evaluation shows that the PrecisePeriod scheduling significantly reduces tail latency while only bringing $$1-2\%$$ 1 - 2 % overhead.

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