There are a lot of ways you _could_ predict this. The simplest might just be to predict the modal value, assuming that these values are IID. If there is some generating process that goes through different phases, and each observation corresponds to a timestep, you might consider fitting an HMM. Others have mentioned sliding windows, and these are fine as well if there is indeed a sequential structure to this data.
How are you trying to use gradient boosting or SVM here? Predicting each in isolation, the best you should be able to do is just predicting the modal value, since you have no further information.
There are a lot of ways you _could_ predict this. The simplest might just be to predict the modal value, assuming that these values are IID. If there is some generating process that goes through different phases, and each observation corresponds to a timestep, you might consider fitting an HMM. Others have mentioned sliding windows, and these are fine as well if there is indeed a sequential structure to this data.
How are you trying to use gradient boosting or SVM here? Predicting each in isolation, the best you should be able to do is just predicting the modal value, since you have no further information.