I have a time series of the amount of apples sold in a specific Region. The time series include monthly values of 10 years (2006-2016). However two months are missing (February 2009 and July 2014). After imputing the data my foremost aim is a forecast for the upcoming months.
The imputeTS package in R includes various methods for my case, e.g. linear, spline, Stineman Interpolation or Kalman Smoothing.
- Which is the most appropriate imputation method in my case?
- How can I make an appropriate forecast although I have missing data?
- Would you recommend another
Rpackage or function?
- The time series has a Unit root in July 2014 and the data in July 2014 is missing. How can I deal with this issue?
Unfortunately most linear about the topic focuses on multivariate time series (Have a look at the Links below).
- What is the intuitive difference between imputation methods for univariate and for multivariate time series?
- How do I have to take this into account when looking at my "apple time series"?
Honaker, J. and King, G. (2010). What to do about missing values in time-series cross-section data What to do about missing values in time-series cross-section data. American Journal of Political Science, 54(2):561–581.
Spratt, M., Carpenter, J., Sterne, J. A. C., Carlin, J. B., Heron, J., Henderson, J., and Tilling, K. (2010). Strategies for multiple imputation in longitudinal studies. American Journal of Epidemiology, 172(4):478–4876.