Large data series: modeling the usual to identify the unusual

Downing, Donald L.; Fedorov, Valerii V.; Lawkins, W.F.; Morris, Max D.; Ostrouchov, George

doi:10.1016/s0167-9473(99)00079-1

Cited by 10 publications

(2 citation statements)

References 9 publications

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“…These authors reported being able to achieve general, brief and accurate summaries in different domains. Some other works [21,22,23] have precisely advocated for segmenting the time dimension and summarize the data values within each sequence, focusing therefore in identifying frequent local cues.…”

Section: Linguistic Summarization and Descriptionmentioning

confidence: 99%

Transcribing Debussy's Syrinx dynamics through Linguistic Description: The MUDELD algorithm

Ros

Molina-Solana

Delgado

et al. 2016

Fuzzy Sets and Systems

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Section: Linguistic Summarization and Descriptionmentioning

confidence: 99%

Transcribing Debussy's Syrinx dynamics through Linguistic Description: The MUDELD algorithm

Ros

Molina-Solana

Delgado

et al. 2016

Fuzzy Sets and Systems

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“…Considering that such time-series data are relatively large or high-frequency, approaches related to sampling and periodicity pose a challenge to standard analytics tools (Varian, 2014). From a statistical viewpoint, the problem that we are looking to address here is more specifically discussed in the work of Downing, Fedorov, Lawkins, Morris, and Ostrouchov (2000). Because of size, there is the assumption that the dataset cannot be analysed at once and should be analysed in segments.…”

mentioning

confidence: 99%

Assessing stationarity in web analytics: A study of bounce rates

2020

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Evidence‐based methods for evaluating marketing interventions such as A/B testing have become standard practice. However, the pitfalls associated with the misuse of this decision‐making instrument are not well understood by managers and analytics professionals. In this study, we assess the impact of stationarity on the validity of samples from conditioned time series, which are abundant in web metrics. Such a prominent metric is the bounce rate, which is prevalent in assessing engagement with web content as well as the performance of marketing touchpoints. In this study, we show how to control for stationarity using an algorithmic transformation to calculate the optimum sampling period. This distance is based on a novel stationary ergodic process that considers that a stationary series presents reversible symmetric features and is calculated using a dynamic time warping algorithm in a self‐correlation procedure. This study contributes to the expert and intelligent systems literature by demonstrating a robust method for sub‐sampling time‐series data, which are critical in decision making.

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Embedding Methods and Robust Statistics for Dimension Reduction

Ostrouchov¹,

Samatova²

2004

COMPSTAT 2004 — Proceedings in Computational Statistics

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Large data series: modeling the usual to identify the unusual

Cited by 10 publications

References 9 publications

Transcribing Debussy's Syrinx dynamics through Linguistic Description: The MUDELD algorithm

Transcribing Debussy's Syrinx dynamics through Linguistic Description: The MUDELD algorithm

Assessing stationarity in web analytics: A study of bounce rates

Embedding Methods and Robust Statistics for Dimension Reduction

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