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Stock price prediction using principal components.
PLoS ONE, 2020 The literature provides strong evidence that stock price values can be predicted from past price data. Principal component analysis (PCA) identifies a small number of principle components that explain most of the variation in a data set.
Mahsa Ghorbani, Edwin K P Chong
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Power swing detecting method using principal components analysis
Energy Reports, 2021 During power swing, the distance protection is easily affected by the oscillations of voltage and current which may lead to the mal-operation of the protection. Therefore, a power swing detecting unit is needed to cooperate with distance protection.
Hao Wang +7 more
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Principal Components of Touch [PDF]
2018 IEEE International Conference on Robotics and Automation (ICRA), 2018 Our human sense of touch enables us to manipulate our surroundings; therefore, complex robotic manipulation will require artificial tactile sensing. Typically tactile sensor arrays are used in robotics, implying that a straightforward way of interpreting multidimensional data is required.
Kirsty Aquilina +2 more
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Principal Components of the Meaning
CoRR, 2020 In this paper we argue that (lexical) meaning in science can be represented in a 13 dimension Meaning Space. This space is constructed using principal component analysis (singular decomposition) on the matrix of word category relative information gains, where the categories are those used by the Web of Science, and the words are taken from a reduced ...
Neslihan Suzen +3 more
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Characteristics of Principal Components in Stock Price Correlation
Frontiers in Physics, 2021 The following methods are used to analyze correlations among stock returns. 1) The meaningful part of the correlation is obtained by applying random matrix theory to the equal-time cross-correlation matrix of assets returns.
Wataru Souma
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Principal Component Projection Without Principal Component Analysis
CoRR, 2016 We show how to efficiently project a vector onto the top principal components of a matrix, without explicitly computing these components. Specifically, we introduce an iterative algorithm that provably computes the projection using few calls to any black-box routine for ridge regression.
Roy Frostig +3 more
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Structural components in functional data. [PDF]
, 2009 Analyzing functional data often leads to finding common factors, for which functional principal component analysis proves to be a useful tool to summarize and characterize the random variation in a function space.
Gasser, T +8 more
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Parameterized principal component analysis [PDF]
Pattern Recognition, 2018 When modeling multivariate data, one might have an extra parameter of contextual information that could be used to treat some observations as more similar to others. For example, images of faces can vary by age, and one would expect the face of a 40 year old to be more similar to the face of a 30 year old than to a baby face.
Ajay Gupta, Adrian Barbu
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COPD phenotype description using principal components analysis
Respiratory Research, 2009 Background Airway inflammation in COPD can be measured using biomarkers such as induced sputum and FeNO. This study set out to explore the heterogeneity of COPD using biomarkers of airway and systemic inflammation and pulmonary function by principal ...
Vestbo Jørgen +5 more
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Interpretability of Composite Indicators Based on Principal Components
Journal of Probability and Statistics, 2022 Principal component approaches are often used in the construction of composite indicators to summarize the information of input variables. The gain of dimension reduction comes at the cost of difficulties in interpretation, inaccurate targeting, and ...
Kris Boudt +3 more
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