Results 1 to 10 of about 54,895 (98)
Time series analyses with psychometric data.
PLoS ONE, 2020 Understanding of interactional dynamics between several processes is one of the most important challenges in psychology and psychosomatic medicine. Researchers exploring behavior or other psychological phenomena mostly deal with ordinal or interval data.
Tatjana Stadnitski
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Proceedings of the ACM on Management of Data, 2023 As a key step of data preparation, it is always necessary to first assert the quality of data before conducting any data application. Given a set of constraints, the validity measure evaluates the degree of data meeting the constraints, e.g., whether the values are in the specified range or fluctuate drastically over time in a series.
Yunxiang Su, Yikun Gong, Shaoxu Song
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Topological Data Analysis for Multivariate Time Series Data
Entropy, 2023 Over the last two decades, topological data analysis (TDA) has emerged as a very powerful data analytic approach that can deal with various data modalities of varying complexities.
Anass B. El-Yaagoubi +2 more
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Challenges of integrated variance estimation in emerging stock markets [PDF]
Zbornik radova Ekonomskog fakulteta u Rijeci : časopis za ekonomsku teoriju i praksu, 2019 Estimating integrated variance, using high frequency data, requires modelling experience and data crunching skills. Although intraday returns have attracted much attention in recent years, handling these data is challenging because of their ...
Josip Arnerić, Mario Matković
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Discretization of Time Series Data [PDF]
Journal of Computational Biology, 2010 An increasing number of algorithms for biochemical network inference from experimental data require discrete data as input. For example, dynamic Bayesian network methods and methods that use the framework of finite dynamical systems, such as Boolean networks, all take discrete input.
Elena S. Dimitrova +3 more
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Predictive analytics beyond time series: Predicting series of events extracted from time series data
Wind Energy, 2022 Realizing carbon neutral energy generation creates the challenge of accurately predicting time‐series generation data for long‐term capacity planning and for short‐term operational decisions.
Sambeet Mishra +3 more
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ACM Computing Surveys, 2012 In almost every scientific field, measurements are performed over time. These observations lead to a collection of organized data called time series . The purpose of time-series data mining is to try to extract all meaningful knowledge from the shape of
Esling, Philippe, Agon, Carlos
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Time series data analysis under indeterminacy
Journal of Big Data, 2023 The existing semi-average method under classical statistics is applied to measure the trend in the time series data. The existing semi-average method cannot be applied when the time series data is in intervals or imprecise.
Muhammad Aslam
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Data Tilting for Time Series [PDF]
Journal of the Royal Statistical Society Series B: Statistical Methodology, 2003 SummaryWe develop a general methodology for tilting time series data. Attention is focused on a large class of regression problems, where errors are expressed through autoregressive processes. The class has a range of important applications and in the context of our work may be used to illustrate the application of tilting methods to interval ...
Hall, Peter, Yao, Qiwei
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Clustering Time Series with Clipped Data [PDF]
Machine Learning, 2005 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Bagnall, Anthony J., Janacek, Gareth J.
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