Bursty Time Series Analysis for Temporal Networks

Jo, Hang-Hyun; Hiraoka, Takayuki

doi:10.1007/978-3-030-23495-9_9

Cited by 8 publications

(3 citation statements)

References 62 publications

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“…The phenomenon may cause the non-stationary variance (heteroskedasticity, also known as volatility), leading to difficulty in sequential modeling [ 9 ]. On that account, volatility modeling has attracted sparked interest in the research community [ 12 , 13 ]. The most common methodologies found in literatures treated the peak points as outliers [ 14 , 15 ] and adopted anomaly detections to identify them.…”

Section: Introductionmentioning

confidence: 99%

A survival analysis based volatility and sparsity modeling network for student dropout prediction

et al. 2022

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Student Dropout Prediction (SDP) is pivotal in mitigating withdrawals in Massive Open Online Courses. Previous studies generally modeled the SDP problem as a binary classification task, providing a single prediction outcome. Accordingly, some attempts introduce survival analysis methods to achieve continuous and consistent predictions over time. However, the volatility and sparsity of data always weaken the models’ performance. Prevailing solutions rely heavily on data pre-processing independent of predictive models, which are labor-intensive and may contaminate authentic data. This paper proposes a Survival Analysis based Volatility and Sparsity Modeling Network (SAVSNet) to address these issues in an end-to-end deep learning framework. Specifically, SAVSNet smooths the volatile time series by convolution network while preserving the original data information using Long-Short Term Memory Network (LSTM). Furthermore, we propose a Time-Missing-Aware LSTM unit to mitigate the impact of data sparsity by integrating informative missingness patterns into the model. A survival analysis loss function is adopted for parameter estimation, and the model outputs monotonically decreasing survival probabilities. In the experiments, we compare the proposed method with state-of-the-art methods in two real-world MOOC datasets, and the experiment results show the effectiveness of our proposed model.

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Section: Introductionmentioning

confidence: 99%

A survival analysis based volatility and sparsity modeling network for student dropout prediction

et al. 2022

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“…The valley durations τ V k are shown in between the arrows. Inspired by [52]. temporal mean µ I and standard deviation σ I of I(t), given as…”

Section: A Interference Pikes and Burstiness Claimmentioning

confidence: 99%

Burstiness of Interference Pikes in Wireless Networks

Atiq

Schilcher

Pengili

et al. 2023

IEEE Open J. Veh. Technol.

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The temporal dynamics of interference in wireless networks affects their performance but has only been studied for some cases. This article addresses this gap by analyzing high-interference events, called pikes, concluding that they arrive in bursts in many cases. Specifically, we show that in Poisson networks with random access and multipath fading, the pike interarrival time increases with the interference correlation, irrespective of the source of correlation and burstiness of pikes. To demonstrate the applicability of this theory, we conduct a measurement campaign with an automotive user in different commercial 4G cellular networks. The experimental results indicate that interference pikes are bursty in the real world as well.

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“…The accumulating of attention by items which are elements of complex evolving systems (e.g., papers receiving citations in scientific communities (Egghe, 2009; Nadarajah & Kotz, 2008) or posts being retweeted on microblogging platforms (Gao et al, 2015)) is a matter often investigated in the literature. Most authors name such processes bursty (starting from Goh and Barabási (2008), where the so‐called burstiness parameter is introduced), compare monographs (Jo & Hiraoka, 2019; Karsai et al, 2018). They occur when short periods of very intensive activity is followed by long periods of inactivity (see Figure 1).…”

Section: Introductionmentioning

confidence: 99%

Time to vote: Temporal clustering of user activity on Stack Overflow

Geras

Siudem

Gągolewski

2022

Asso for Info Science & Tech

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Question‐and‐answer (Q&A) sites improve access to information and ease transfer of knowledge. In recent years, they have grown in popularity and importance, enabling research on behavioral patterns of their users. We study the dynamics related to the casting of 7 M votes across a sample of 700 k posts on Stack Overflow, a large community of professional software developers. We employ log‐Gaussian mixture modeling and Markov chains to formulate a simple yet elegant description of the considered phenomena. We indicate that the interevent times can naturally be clustered into 3 typical time scales: those which occur within hours, weeks, and months and show how the events become rarer and rarer as time passes. It turns out that the posts' popularity in a short period after publication is a weak predictor of its overall success, contrary to what was observed, for example, in case of YouTube clips. Nonetheless, the sleeping beauties sometimes awake and can receive bursts of votes following each other relatively quickly.

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Bursty Time Series Analysis for Temporal Networks

Cited by 8 publications

References 62 publications

A survival analysis based volatility and sparsity modeling network for student dropout prediction

A survival analysis based volatility and sparsity modeling network for student dropout prediction

Burstiness of Interference Pikes in Wireless Networks

Time to vote: Temporal clustering of user activity on Stack Overflow

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