I have encountered four consecutive years, several consecutive months in a year and dates in a month of missing observed temperature data. How can I fill the missing data?
You can approximate it by using regression.
I hope the references below, will help you.
References:
http://www.stat.columbia.edu/~gelman/arm/missing.pdf
http://www.biostat.jhsph.edu/~iruczins/teaching/jf/ch14.pdf
http://www.measuringu.com/blog/handle-missing-data.php
Difficult to help simply - I have seen whole courses related to missing data treatment (in IBM SPSS), as there are more methods...
http://www.ats.ucla.edu/stat/spss/modules/missing.htm
http://www.sussex.ac.uk/its/pdfs/SPSS_Missing_Values_22.pdf
Hi Efrem
A simple method is using R, and mi package, this solution used a multiple imputation for missing values.
You can check this source: https://cran.r-project.org/web/packages/mi/mi.pdf
Hi Efrem,
You may find this relevant:
Wesonga, R. (2015). On multivariate imputation and forecasting of decadal wind speed missing data. SpringerPlus, 4(1), 1.
http://springerplus.springeropen.com/articles/10.1186/s40064-014-0774-9
Good luck
If you ignore the time dependency, you could use the R packages VIM or mi, for example. Otherwise, I think the Amelia (Amelia2) package in R can be used for it.
- Badges
- Science topic
- Similar topics
- Medicine
- Physiology
- Biosignals
- Biomedical Signal Processing
- Statistical Analysis