Temporal Outlier Detection and Correlation Analysis of Business Process Executions

Park, Chun Gun; Ahn, Hyun

doi:10.1587/transinf.2018edl8246

Cited by 3 publications

(1 citation statement)

References 6 publications

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“…Among the wide array of data classes, time series, which is a sequence of data arranged in chronological order (i.e., a typical type in data analysis), has many real-world applications in various domains, such as energy [1,2], climate [3], economics [4], business [5] and healthcare [6]. The use of a significant amount of time series data that can be obtained via sensors and computing devices that have evolved in recent years might enhance analysis and forecasting abilities for solving real-life problems.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Missing Data Imputation Methods in Time Series Forecasting

Ahn¹,

Sun²,

Kim³

2022

Computers, Materials &Amp; Continua

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Time series forecasting has become an important aspect of data analysis and has many real-world applications. However, undesirable missing values are often encountered, which may adversely affect many forecasting tasks. In this study, we evaluate and compare the effects of imputation methods for estimating missing values in a time series. Our approach does not include a simulation to generate pseudo-missing data, but instead perform imputation on actual missing data and measure the performance of the forecasting model created therefrom. In an experiment, therefore, several time series forecasting models are trained using different training datasets prepared using each imputation method. Subsequently, the performance of the imputation methods is evaluated by comparing the accuracy of the forecasting models. The results obtained from a total of four experimental cases show that the k-nearest neighbor technique is the most effective in reconstructing missing data and contributes positively to time series forecasting compared with other imputation methods.

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Section: Introductionmentioning

confidence: 99%

Comparison of Missing Data Imputation Methods in Time Series Forecasting

Ahn¹,

Sun²,

Kim³

2022

Computers, Materials &Amp; Continua

Self Cite

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A Real-Time Method for Detecting Temporary Process Variants in Event Log Data

Chouhan

Wilbik

Dijkman

2021

Lecture Notes in Computer Science

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Time-aware outlier detection in health physique monitoring in edge-aided sport education decision-makings

Li,

Kang,

et al. 2024

J Cloud Comp

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The increasing popularity of various intelligent sensor and mobile communication technologies has enabled quick health physique sensing, monitoring, collection and analyses of students, which significantly promoted the development of sport education. Through collecting the students’ physiological signals and transmitted them to edge servers, we can precisely analyze and judge whether a student is in poor health (e.g., an outlier). However, with time elapsing, the accumulated physiological signals of students become massive, which places a heavy burden on the quick storage and in-time processing of physiological data of students. In this situation, it is becoming a necessity to develop a time-aware outlier detection technique for health physique evaluation of students in a time-efficient way. Considering this challenge, we propose a novel time-aware outlier detection method named TOD based on Locality-Sensitive Hashing. TOD condenses extensive physiological student data into a concise set of health indices. Leveraging these indices, we can efficiently identify potential student outliers from a large pool of candidates with precision and speed. Finally, we have designed a group of simulated experiments based on WS-DREAM dataset. Experiment results prove the feasibility and superiority of the TOD method compared with other existing methods.

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Temporal Outlier Detection and Correlation Analysis of Business Process Executions

Cited by 3 publications

References 6 publications

Comparison of Missing Data Imputation Methods in Time Series Forecasting

Comparison of Missing Data Imputation Methods in Time Series Forecasting

A Real-Time Method for Detecting Temporary Process Variants in Event Log Data

Time-aware outlier detection in health physique monitoring in edge-aided sport education decision-makings

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