Effects of missing data imputation methods on univariate blood pressure time series data analysis and forecasting with ARIMA and LSTM

Effects of missing data imputation methods on univariate blood pressure time series data analysis and forecasting with ARIMA and LSTM

Abstract Background Missing observations within the univariate time series are common in real-life and cause analytical problems in the flow of the analysis. Imputation of missing values is an inevitable step in every incomplete univariate time series. Most of the existing studies focus on comparing...

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Bibliographic Details
Main Authors: Nicholas Niako, Jesus D. Melgarejo, Gladys E. Maestre, Kristina P. Vatcheva
Format: Article
Language:English
Published: BMC 2024-12-01
Series:BMC Medical Research Methodology
Subjects:
Univariate time series
Missing data imputation
ARIMA
LSTM
Forecasting
Ambulatory blood pressure
Online Access:https://doi.org/10.1186/s12874-024-02448-3
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https://doi.org/10.1186/s12874-024-02448-3

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