imputeFin-package: imputeFin: Imputation of Financial Time Series with Missing...

In imputeFin: Imputation of Financial Time Series with Missing Values and/or Outliers

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.

Functions

fit_AR1_Gaussian, impute_AR1_Gaussian, fit_AR1_t, impute_AR1_t, plot_imputed

Data

ts_AR1_Gaussian, ts_AR1_t

Help

For a quick help see the README file: GitHub-README.

For more details see the vignette: CRAN-vignette.

Author(s)

Junyan Liu, Rui Zhou, and Daniel P. Palomar

References

J. Liu, S. Kumar, and D. P. Palomar, "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, Apr. 2019. <https://doi.org/10.1109/TSP.2019.2899816>

R. Zhou, J. Liu, S. Kumar, and D. P. Palomar, "Student’s t VAR Modeling with Missing Data via Stochastic EM and Gibbs Sampling," IEEE Trans. on Signal Processing, vol. 68, pp. 6198-6211, Oct. 2020. <https://doi.org/10.1109/TSP.2020.3033378>

imputeFin documentation built on Feb. 20, 2021, 9:07 a.m.