The application provides a risk prediction
window selection method and device based on multi-target cooperative evaluation and
electronic equipment, relates to the technical field of
machine learning and
data modeling, and comprises the following steps: obtaining
time sequence historical data of a target object, and dividing the
time sequence historical data into a training subset and a test subset used for simulating future prediction according to
time sequence; a candidate prediction window set containing at least two different time lengths is set, for each window length in the set, a corresponding
binary classification prediction task
label is defined based on the length, and a corresponding prediction model is trained by using the training subset; taking the model performance corresponding to the longest
reference window in the candidate window as a reference, the optimal window length is selected from the candidate window set according to a preset
selection criterion. The application ensures that the selected window length directly serves the accuracy target of the final prediction model, so that the shortest effective window is automatically found under the premise of ensuring that the prediction performance of the long window
reference model is equivalent.