  KDD CUP 2022 proposes a time-series forecasting task on spatial dynamic wind
power dataset, in which the participants are required to predict the future
generation given the historical context factors. The evaluation metrics contain
RMSE and MAE. This paper describes the solution of Team 88VIP, which mainly
comprises two types of models: a gradient boosting decision tree to memorize
the basic data patterns and a recurrent neural network to capture the deep and
latent probabilistic transitions. Ensembling these models contributes to tackle
the fluctuation of wind power, and training submodels targets on the
distinguished properties in heterogeneous timescales of forecasting, from
minutes to days. In addition, feature engineering, imputation techniques and
the design of offline evaluation are also described in details. The proposed
solution achieves an overall online score of -45.213 in Phase 3.
