Imputation of Financial Time Series with Missing Values and/or Outliers
Fit Gaussian AR(1) model to time series with missing values and/or out...
Fit Student's t AR(1) model to time series with missing values and/or ...
Fit Student's t VAR model to time series with missing values and/or ou...
Impute missing values of time series based on a Gaussian AR(1) model
Impute missing values of time series based on a Student's t AR(1) mode...
Impute missing values of an OHLC time series on a rolling window basis...
Impute missing values of time series on a rolling window basis based o...
imputeFin: Imputation of Financial Time Series with Missing Values.
Plot imputed time series.
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>.
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