R中时间序列的保正插值

Question

I have some data with missing values that I know to be positive. I'm trying to interpolate the missing values using na.interp from the forecast package. However, some of the interpolated values turn out to be negative.

I've tried na.approx from the package zoo , but the approximated values do not agree with the seasonal trend of the time series.

I cannot interpolate in the log domain since some of my observations are 0 . Interpolating in the square-root domain somehow produces too many outliers. Is there any other way to interpolate time series while preserving positivity? Any references to other R packages would also be appreciated.

Answer 1

There is the imputeTS package, which specifically focuses on missing values in time series. (take a look at this Paper )

It works like this:

na.kalman(yourTimeSeries)

That's it already.

It offers several time series imputation functions:

• Imputation by Linear Interpolation
• Imputation by Spline Interpolation
• Imputation by Stineman Interpolation
• Imputation by Structural Model & Kalman Smoothing
• Imputation by ARIMA State Space Representation & Kalman Sm.
• Imputation by Last Observation Carried Forward
• Imputation by Next Observation Carried Backward
• Missing Value Imputation by Simple Moving Average
• Imputation by Linear Weighted Moving Average
• Imputation by Exponential Weighted Moving Average
• Missing Value Imputation by Mean Value
• Seasonally Decomposed Missing Value Imputation
• Seasonally Splitted Missing Value Imputation

Some of these functions are more advanced some are less advanced. I would try the na.kalman() function of the package for this task. Might be that the results of this function already adhere the constraints. Otherwise you need to perform some transformations before performing the imputation (as explained below).

In general if you want your imputation to be constrained to some bounds this transformation approach might also help:

library("imputeTS")

# Bounds
a <- 50
b <- 400

# Transform data
y <- log((myTimeSeries-a)/(b-myTimeSeries))
imputations <- na.kalman(y)

# Back-transform
imputationsBack <- (b-a)*exp(imputations)/(1+exp(imputations)) + a