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  "Title": "Imputation of Financial Time Series with Missing Values and/or\nOutliers",
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  "Date": "2021-02-19",
  "Description": "Missing values often occur in financial data due to a\nvariety of reasons (errors in the collection process or in the\nprocessing stage, lack of asset liquidity, lack of reporting of\nfunds, etc.). However, most data analysis methods expect\ncomplete data and cannot be employed with missing values. One\nconvenient way to deal with this issue without having to\nredesign the data analysis method is to impute the missing\nvalues. This package provides an efficient way to impute the\nmissing values based on modeling the time series with a random\nwalk or an autoregressive (AR) model, convenient to model\nlog-prices and log-volumes in financial data. In the current\nversion, the imputation is univariate-based (so no asset\ncorrelation is used). In addition, outliers can be detected and\nremoved. The package is based on the paper: J. Liu, S. Kumar,\nand D. P. Palomar (2019). Parameter Estimation of Heavy-Tailed\nAR Model With Missing Data Via Stochastic EM. IEEE Trans. on\nSignal Processing, vol. 67, no. 8, pp. 2159-2172.\n<doi:10.1109/TSP.2019.2899816>.",
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  "Date/Publication": "2021-09-27 06:43:44 UTC",
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