ARMAimpute: Predicts missing values of a time series based on an ARMA...

In gregorkb/tscourse: Contains several functions for time series analysis.

Predicts missing values of a time series based on an ARMA model

Description

Predicts missing values of a time series based on an ARMA model

Usage

ARMAimpute(X, phi, theta, sigma, plot = TRUE)

Arguments

X	a vector containing time series data with missing values.
phi	a vector with autoregressive coefficients.
theta	a vector the moving average coefficients.
sigma	the white noise variance.

Value

a list containing the predicted values of the time series, upper and lower bounds for the 95 This function predicts or imputes missing values in a time series by constructing an autocovariance function out of given ARMA parameters. One should find estimated for the ARMA parameters based on the observed data using the arima() function and then feed these into this function to get the predictions for the missing values.

gregorkb/tscourse documentation built on Oct. 3, 2022, 5:31 p.m.