A New Correlation Coefficient for Bivariate Time-Series Data

Erdem, Orhan; Ceyhan, Elvan; Varlı, Yusuf

doi:10.2139/ssrn.2390065

Cited by 6 publications

(10 citation statements)

References 11 publications

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“…Pearson correlation coefficient (R) is used to evaluate the correlation between two pairs of input and output variables, and it is the most widely used correlation function to find the degree of the relationship between linear variables. Pearson correlation coefficient can be calculated by using Equation () 45

R = \frac{\sum_{i = 1}^{n} ((), x_{i} - \overset{x}{true}) ((), y_{i} - \overset{y}{true})}{\sqrt{\sum_{i = 1}^{n} {(x_{i} - \overset{false¯}{x})}^{2}} \sqrt{\sum_{i = 1}^{n} {(y_{i} - \overset{false¯}{y})}^{2}}}

where n is the number of samples, x i , y i are the single samples indexed with i , and

\overset{x}{true}, \overset{y}{true}

are the means of samples.…”

Section: Methodsmentioning

confidence: 99%

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Correlation analysis and MLP / CMLP for optimum variables to predict orientation and tilt angles in intelligent solar tracking systems

2020

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R = \frac{\sum_{i = 1}^{n} ((), x_{i} - \overset{x}{true}) ((), y_{i} - \overset{y}{true})}{\sqrt{\sum_{i = 1}^{n} {(x_{i} - \overset{false¯}{x})}^{2}} \sqrt{\sum_{i = 1}^{n} {(y_{i} - \overset{false¯}{y})}^{2}}}

where n is the number of samples, x i , y i are the single samples indexed with i , and

\overset{x}{true}, \overset{y}{true}

are the means of samples.…”

Section: Methodsmentioning

confidence: 99%

“…In some cases, the values of |R| = 0.3 are adopted to denote a moderate relationship between the pairs of variables. 44,45 In other cases, the values of |R| = 0.4 are adopted to denote a moderate relationship as well. 46,48 Pearson correlation coefficient of zero value indicates no linear relationship between input and output variables.…”

Section: R =mentioning

confidence: 99%

Correlation analysis and MLP / CMLP for optimum variables to predict orientation and tilt angles in intelligent solar tracking systems

2020

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“…In this experiment, let the two functions perform on ASL dataset in which the lengths of MTS are different. The two parameters w and α used in the two functions are set to be w = [2,3,4,5,6,7,8] and α = [3,4,5,6,7,8,9,10]. Their tightness can be seen in Figs.…”

Section: Tightness Of Lower-bound Functionsmentioning

confidence: 99%

“…In addition, some distance functions [3][4][5][6] are also designed to improve the quality of time series data mining. Some techniques of dimensionality reduction often combine with their corresponding distance functions [7][8][9] or correlations [10] for measuring the similarity between two different representations.…”

Section: Introductionmentioning

confidence: 99%

Piecewise aggregate representations and lower-bound distance functions for multivariate time series

2015

Physica A: Statistical Mechanics and its Applications

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“…Pearson Correlation Coefficient (PCC) is the most used similarity measure [ 16 – 18 ], although alternative correlation functions can be also employed [ 19 – 21 ]. However, PCC lacks sensitivity in case of non-linear relations [ 22 ] and time shift between signals [ 23 – 25 ], thus the reliability of a coexpression network would benefit from a measure taking care of these characteristics.…”

Section: Introductionmentioning

confidence: 99%

DTW-MIC Coexpression Networks from Time-Course Data

et al. 2016

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When modeling coexpression networks from high-throughput time course data, Pearson Correlation Coefficient (PCC) is one of the most effective and popular similarity functions. However, its reliability is limited since it cannot capture non-linear interactions and time shifts. Here we propose to overcome these two issues by employing a novel similarity function, Dynamic Time Warping Maximal Information Coefficient (DTW-MIC), combining a measure taking care of functional interactions of signals (MIC) and a measure identifying time lag (DTW). By using the Hamming-Ipsen-Mikhailov (HIM) metric to quantify network differences, the effectiveness of the DTW-MIC approach is demonstrated on a set of four synthetic and one transcriptomic datasets, also in comparison to TimeDelay ARACNE and Transfer Entropy.

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A New Correlation Coefficient for Bivariate Time-Series Data

Cited by 6 publications

References 11 publications

Correlation analysis and MLP / CMLP for optimum variables to predict orientation and tilt angles in intelligent solar tracking systems

Correlation analysis and MLP / CMLP for optimum variables to predict orientation and tilt angles in intelligent solar tracking systems

Piecewise aggregate representations and lower-bound distance functions for multivariate time series

DTW-MIC Coexpression Networks from Time-Course Data

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