Modelling and Analysis of Volatility in Time Series Data

Somarajan, Siddarth; Shankar, Monica; Sharma, Tanmay; Jeyanthi, R.

doi:10.1007/978-981-13-3393-4_62

Cited by 12 publications

(6 citation statements)

References 12 publications

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“…First, the volatility in data may cause heteroskedasticity, which affects the model reliability [ 9 ]. In recent years, in some human-related application scenarios such as MOOCs, it has been independently observed that a series of user actions burst together in a short period, making peaks randomly appear in time series [ 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, in some human-related application scenarios such as MOOCs, it has been independently observed that a series of user actions burst together in a short period, making peaks randomly appear in time series [ 10 , 11 ]. 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 ].…”

Section: Introductionmentioning

confidence: 99%

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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%

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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“…Additional benefits of SLIM include allowing a visual comparison of relative volatility levels within and between series (an important consideration, especially in finance as volatility furnishes key aspects, such as return on investments and effective hedging [27]). The identification of localised sub-sequences distinguishing volatility related to sudden large events from a consistent increase, for example, is also facilitated by SLIM.…”

Section: Contributionmentioning

confidence: 99%

Side-Length-Independent Motif (SLIM): Motif Discovery and Volatility Analysis in Time Series—SAX, MDL and the Matrix Profile

2022

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As the availability of big data-sets becomes more widespread so the importance of motif (or repeated pattern) identification and analysis increases. To date, the majority of motif identification algorithms that permit flexibility of sub-sequence length do so over a given range, with the restriction that both sides of an identified sub-sequence pair are of equal length. In this article, motivated by a better localised representation of variations in time series, a novel approach to the identification of motifs is discussed, which allows for some flexibility in side-length. The advantages of this flexibility include improved recognition of localised similar behaviour (manifested as motif shape) over varying timescales. As well as facilitating improved interpretation of localised volatility patterns and a visual comparison of relative volatility levels of series at a globalised level. The process described extends and modifies established techniques, namely SAX, MDL and the Matrix Profile, allowing advantageous properties of leading algorithms for data analysis and dimensionality reduction to be incorporated and future-proofed. Although this technique is potentially applicable to any time series analysis, the focus here is financial and energy sector applications where real-world examples examining S&P500 and Open Power System Data are also provided for illustration.

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“…Historical volatility (Hong and Lee, 2017), (Somarajan et al, 2019) is a statistical measure which is widely used in applications in economics and finance. It is used by analysts and stock traders as part of the creation of financial investing strategies.…”

Section: Conversational Volatilitymentioning

confidence: 99%

An Analysis of Conversational Volatility During Telecollaboration Sessions for Second Language Learning

Smeaton

Dey-Plissonneau

Lee

et al. 2022

8th International Conference on Higher Education Advances (HEAd'22)

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Tandem telecollaboration is a pedagogy used in second language learning where mixed groups of students meet online in videoconferencing sessions to practice their conversational skills in their target language. We have built and deployed a system called L2 Learning to support post-session review and self-reflection on students’ participation in such meetings. We automatically compute a metric called Conversational Volatility which quantifies the amount of interaction among participants, indicating how dynamic or flat the conversations were. Our analysis on more than 100 hours of video recordings involving 28 of our students indicates that conversations do not get more dynamic as meetings progress, that there is a wide variety of levels of interaction across students and student groups, and the speaking in French appears to have more animated conversations than speaking in English, though the reasons for that are not clear.

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Modelling and Analysis of Volatility in Time Series Data

Cited by 12 publications

References 12 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

Side-Length-Independent Motif (SLIM): Motif Discovery and Volatility Analysis in Time Series—SAX, MDL and the Matrix Profile

An Analysis of Conversational Volatility During Telecollaboration Sessions for Second Language Learning

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