Imputation of Financial Time Series with Missing Values and/or
Outliers
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. <doi:10.1109/TSP.2019.2899816>.
c(
person(c("Daniel", "P."), "Palomar", role = c("cre", "aut"), email = "daniel.p.palomar@gmail.com"),
person("Junyan", "Liu", role =  "aut", email = "jliubl@connect.ust.hk")
)
knitr, ggplot2, prettydoc, rmarkdown, R.rsp, testthat, xts
Daniel P. Palomar [cre, aut],
Junyan Liu [aut]

c(
person(c("Daniel", "P."), "Palomar", role = c("cre", "aut"), email = "daniel.p.palomar@gmail.com"),
person("Junyan", "Liu", role =  "aut", email = "jliubl@connect.ust.hk"),
person("Rui", "Zhou", role =  "aut", email = "rui.zhou@connect.ust.hk")
)
https://CRAN.R-project.org/package=imputeFin,
https://github.com/dppalomar/imputeFin,
https://www.danielppalomar.com,
https://doi.org/10.1109/TSP.2019.2899816,
https://doi.org/10.1109/TSP.2020.3033378
Daniel P. Palomar [cre, aut],
Junyan Liu [aut],
Rui Zhou [aut]

Ease the Implementation of Imputation Methods
person("Tobias", "Rockel", , "Rockel.To@gmail.com", role = c("aut", "cre"))
The general workflow of most imputation methods is quite
similar. The aim of this package is to provide parts of this general
workflow to make the implementation of imputation methods easier. The
heart of an imputation method is normally the used model. These models
can be defined using the 'parsnip' package or customized
specifications. The rest of an imputation method are more technical
specification e.g. which columns and rows should be used for
imputation and in which order. These technical specifications can be
set inside the imputation functions.