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imputeFin  

Imputation of Financial Time Series with Missing Values and/or Outliers
View on CRAN: Click here


Download and install imputeFin package within the R console
Install from CRAN:
install.packages("imputeFin")

Install from Github:
library("remotes")
install_github("cran/imputeFin")

Install by package version:
library("remotes")
install_version("imputeFin", "0.1.2")



Attach the package and use:
library("imputeFin")
Maintained by
Daniel P. Palomar
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2019-12-13
Latest Update: 2021-02-20
Description:
Missing values often occur in financial data due to a variety of reasons (errors in the collection process or in the processing stage, lack of asset liquidity, lack of reporting of funds, etc.). However, most data analysis methods expect complete data and cannot be employed with missing values. One convenient way to deal with this issue without having to redesign the data analysis method is to impute the missing values. This package provides an efficient way to impute the missing values based on modeling the time series with a random walk or an autoregressive (AR) model, convenient to model log-prices and log-volumes in financial data. In the current version, the imputation is univariate-based (so no asset correlation is used). In addition, outliers can be detected and removed. The package is based on the paper: J. Liu, S. Kumar, and D. P. Palomar (2019). Parameter Estimation of Heavy-Tailed AR Model With Missing Data Via Stochastic EM. IEEE Trans. on Signal Processing, vol. 67, no. 8, pp. 2159-2172. .
How to cite:
Daniel P. Palomar (2019). imputeFin: Imputation of Financial Time Series with Missing Values and/or Outliers. R package version 0.1.2, https://cran.r-project.org/web/packages/imputeFin. Accessed 22 Dec. 2024.
Previous versions and publish date:
0.1.0 (2019-12-13 16:00), 0.1.1 (2020-07-11 15:10)
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