I am working with some climate data where I want to predict crop yields with my dataset containing climate- and satellite-derived features.
This is a time series regression forecasting problem and I want to put it through XGBoost and Lasso to generate my predictions. However, there is a mismatch in the sample frequency between my features and target variable; the features are recorded weekly, and the target variable is recorded yearly.
At the moment, I am using a wide-format table as my input dataset to the ML models, but I feel like my models are not generating accurate predictions when the input table is formatted with the wide-format, since there are less samples compared to if I used a long-format table.
Data Table Reference:
For reference, the wide format table looks something like this, where the suffix represents a week number on a feature:
And the long format table would look something like this:
Would it be advisable to use the long format table as the input to my ML models? I feel like the identical crop yields for each associated ID and year would throw my models off.
In addition, is there a better way to frame my data that I haven't explored yet?