Package 'imputeFin'
| Title: |
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. 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>. |
| Authors: |
Daniel P. Palomar [cre, aut],
Junyan Liu [aut],
Rui Zhou [aut] |
| Maintainer: |
Daniel P. Palomar <[email protected]> |
| License: |
GPL-3 |
| Version: |
0.1.2.9000 |
| Built: |
2024-11-09 05:23:42 UTC |
| Source: |
https://github.com/dppalomar/imputefin |
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