Replacing last line missing value for time series
Hi everyone,
I just want to do a simple prediction for our company sale numbers. I have data from 2012 to 2020 for one of our product and i just want to predict 2021 year sale number so i can compare it with the real sale numbers.
I just do not want to do it with replacing missing numbers with average or other methods. Tried to do it with "replacing missing numbers (time series)" operator with linear interpolation but it seems it can not replace the last line of missing value. How can i solve this or should i use knn, decision tree or neural network to predict it?
I just want to do a simple prediction for our company sale numbers. I have data from 2012 to 2020 for one of our product and i just want to predict 2021 year sale number so i can compare it with the real sale numbers.
I just do not want to do it with replacing missing numbers with average or other methods. Tried to do it with "replacing missing numbers (time series)" operator with linear interpolation but it seems it can not replace the last line of missing value. How can i solve this or should i use knn, decision tree or neural network to predict it?
If we manage to predict number i also want to compare the methods' prediction values. i would appreciate if it is a simple solution like (hard to understand and explain root mean squared error to boss for example) comparing the prediction values how close to real numbers.
Maybe i did all the process wrong but ty for your answers i am still trying to learn.
Maybe i did all the process wrong but ty for your answers i am still trying to learn.