In our workflows, the target is always unique for each location/year. However, sometimes the predictors are timeseries for the same point/year. We need a way to represent time series as input features.
Currently, our approach is to pivot the dataframe, such that each day/month/entry in the timeseries is one column. This leads to many columns with complex names, such as
year
geometry
tmax\
1
tmax\
2
tmax\
3
...
tmin\
9
tmin\
10
tmin\
11
tmin\
12
Alternatively, we could
Store time series as such in a single pandas cell
Switch to xarray or something similar to better represent multidimensional data
In our workflows, the target is always unique for each location/year. However, sometimes the predictors are timeseries for the same point/year. We need a way to represent time series as input features.
Currently, our approach is to pivot the dataframe, such that each day/month/entry in the timeseries is one column. This leads to many columns with complex names, such as
Alternatively, we could