Imputation of Missing Values in Economic and Financial Time Series Data Using Five Principal Component Analysis (PCA) Approaches

John, Chisimkwuo; Ekpenyong, Emmanuel John; Nworu, Charles Chinedu

doi:10.33429/cjas.10119.3/6

Cited by 10 publications

(8 citation statements)

References 18 publications

(18 reference statements)

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“…We selected eight representative methods for comparative assessment, based on their intended missing mechanism(s) and imputation principles (summarized in Figure 1). One method (Min/2) is devoted to MNAR (LLD) 7 , two methods (swKNN and pwKNN) are tailored to MAR (local-similarity) 16 , and five methods (Mean, PPCA, NIPALS, SVD, and SVT) are intended for MCAR/MAR (global-structure or low-rank matrix factorization) 7,10,[17][18][19] . We then explored and tested several variants of FRMF and CAM, where local similarity information is obtained from baseline or other data acquired from the same samples.…”

Section: Experimental Design and Protocolmentioning

confidence: 99%

Comparative Assessment and Novel Strategy on Methods for Imputing Proteomics Data

Chang

Chao

Parker

et al. 2021

Preprint

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Missing values are a major issue in quantitative proteomics analysis. While many methods have been developed for imputing missing values in high-throughput proteomics data, a comparative assessment of imputation accuracy remains inconclusive, mainly because mechanisms contributing to true missing values are complex and existing evaluation methodologies are imperfect. Moreover, few studies have provided an outlook of future methodological development. We first re-evaluate the performance of eight representative methods targeting three typical missing mechanisms. These methods are compared on both simulated and masked missing values embedded within real proteomics datasets, and performance is evaluated using three quantitative measures. We then introduce fused regularization matrix factorization, a low-rank global matrix factorization framework, capable of integrating local similarity derived from additional data types. We also explore a biologically-inspired latent variable modeling strategy - convex analysis of mixtures - for missing value imputation and present preliminary experimental results. While some winners emerged from our comparative assessment, the evaluation is intrinsically imperfect because performance is evaluated indirectly on artificial missing or masked values not authentic missing values. Nevertheless, we show that our fused regularization matrix factorization provides a novel incorporation of external and local information, and the exploratory implementation of convex analysis of mixtures presents a biologically plausible new approach.

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Section: Experimental Design and Protocolmentioning

confidence: 99%

Comparative Assessment and Novel Strategy on Methods for Imputing Proteomics Data

Chang

Chao

Parker

et al. 2021

Preprint

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“…One method (Min/2) is devoted to MNAR (LLD) [7], two methods (swKNN and pwKNN) are tailored to MAR (local-similarity) [15], and five methods (Mean, PPCA, NIPALS, SVD, and SVT) are designed for MCAR/MAR (global-structure or low-rank matrix factorization) [7,9,[16][17][18]. We then explored and tested several variants of FRMF and CAM, where local similarity information is obtained from baseline or other data acquired from the same samples.…”

Section: Experimental Design and Protocolmentioning

confidence: 99%

Comparative Assessment and Outlook on Methods for Imputing Proteomics Data

Chang

Chao

Parker

et al. 2021

Preprint

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Background: Missing values are a major issue in quantitative proteomics data analysis. While many methods have been developed for imputing missing values in high-throughput proteomics data, comparative assessment on the accuracy of existing methods remains inconclusive, mainly because the true missing mechanisms are complex and the existing evaluation methodologies are imperfect. Moreover, few studies have provided an outlook of current and future development.Results: We first report an assessment of eight representative methods collectively targeting three typical missing mechanisms. The selected methods are compared on both realistic simulation and real proteomics datasets, and the performance is evaluated using three quantitative measures. We then discuss fused regularization matrix factorization, a popular low-rank matrix factorization framework with similarity and/or biological regularization, which is extendable to integrating multi-omics data such as gene expressions or clinical variables. We further explore the potential application of convex analysis of mixtures, a biologically-inspired latent variable modeling strategy, to missing value imputation. The preliminary results on proteomics data are provided together with an outlook into future development directions.Conclusion: While a few winners emerged from our comparative assessment, data-driven evaluation of imputation methods is imperfect because performance is evaluated indirectly on artificial missing or masked values not authentic missing values. Imputation accuracy may vary with signal intensity. Fused regularization matrix factorization provides a possibility of incorporating external information. Convex analysis of mixtures presents a biologically plausible new approach.

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“…Vast amounts of data available in the recent years have pushed some of the above discussed GC extensions, information and phase-space reconstruction based approaches forward as they rely on joint probability density estimations, stationarity, markovianity, topological or linear modeling. However, still, many temporal observations made in various domains such as climatology 34 , 35 , finance 36 , 37 and sociology 38 are often short in length, have missing samples or are irregularly sampled. A significant challenge arises when we attempt to apply causality measures in such situations 11 .…”

Section: Introductionmentioning

confidence: 99%

Compression complexity with ordinal patterns for robust causal inference in irregularly sampled time series

Kathpalia

Manshour

Paluš

2022

Sci Rep

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Distinguishing cause from effect is a scientific challenge resisting solutions from mathematics, statistics, information theory and computer science. Compression-Complexity Causality (CCC) is a recently proposed interventional measure of causality, inspired by Wiener–Granger’s idea. It estimates causality based on change in dynamical compression-complexity (or compressibility) of the effect variable, given the cause variable. CCC works with minimal assumptions on given data and is robust to irregular-sampling, missing-data and finite-length effects. However, it only works for one-dimensional time series. We propose an ordinal pattern symbolization scheme to encode multidimensional patterns into one-dimensional symbolic sequences, and thus introduce the Permutation CCC (PCCC). We demonstrate that PCCC retains all advantages of the original CCC and can be applied to data from multidimensional systems with potentially unobserved variables which can be reconstructed using the embedding theorem. PCCC is tested on numerical simulations and applied to paleoclimate data characterized by irregular and uncertain sampling and limited numbers of samples.

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Imputation of Missing Values in Economic and Financial Time Series Data Using Five Principal Component Analysis (PCA) Approaches

Cited by 10 publications

References 18 publications

Comparative Assessment and Novel Strategy on Methods for Imputing Proteomics Data

Comparative Assessment and Novel Strategy on Methods for Imputing Proteomics Data

Comparative Assessment and Outlook on Methods for Imputing Proteomics Data

Compression complexity with ordinal patterns for robust causal inference in irregularly sampled time series

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