imputeFin

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).

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. https://doi.org/10.1109/TSP.2019.2899816

Installation

The package can be installed from CRAN or GitHub:

# install stable version from CRAN
install.packages("imputeFin")

# install development version from GitHub
devtools::install_github("dppalomar/imputeFin")

To get help:

library(imputeFin)
help(package = "imputeFin")
?impute_AR1_Gaussian

To cite imputeFin in publications:

citation("imputeFin")

Quick Start

Let’s load some time series data with missing values for illustration purposes:

library(imputeFin)
data(ts_AR1_Gaussian)
names(ts_AR1_Gaussian)
#> [1] "y_missing" "phi0"      "phi1"      "sigma2"

We can then impute one of the time series and plot it:

y_missing <- ts_AR1_Gaussian$y_missing[, 3]
y_imputed <- impute_AR1_Gaussian(y_missing)
plot_imputed(y_imputed)

Package: CRAN and GitHub.

README file: GitHub-readme.