I have a dataset containing data on temperature, precipitation, and soybean yields for a farm for 10 years (2005 - 2014). I would like to predict yields for 2015 based on this data.
Please note that the dataset has DAILY values for temperature and precipitation, but only 1 value per year for the yield (since harvesting of crop happens at end of growing season of crop).
I would like to build a regression or some other machine learning based model to predict 2015 yields, based on a regression/some other model derived by studying the relation between yields and temperature and precipitation in previous years.
As per, Building a machine learning model to predict crop yields based on environmental data, I am using sklearn.cross_validation.LabelKFold
to assign each year the same label.
The question is that since I have a single target value per year, do I need to interpolate to fill in target values for all the other days of the year? Should I just use the same target value for each day of the year?