| 讲座简介： | The decomposition-ensemble algorithm has received increasing attention in forecast and related fields, especially in capturing the nonlinear and nonstationary characteristics of time series data. A conventional strategy involves decomposing the target time series into various oscillation modes from the frequency domain and assigning equal weights to all decomposed modes for aggregated prediction. However, disparities in forecasting performance arise among different decomposed modes due to their distinct attributes and forecast horizons. This paper proposes a novel forward-validation model averaging approach to combine decomposed modes with appropriate weights, thereby enhancing the accuracy of the target time series forecast. It is shown that the proposed model averaging estimator is asymptotically optimal in the sense of achieving the lowest possible quadratic prediction risk. The rate of the selected weights converging to the optimal weights to minimizing the expected quadratic loss is established. Simulation studies and empirical applications to consumption and exchange rate forecasting highlight the merits of the proposed method. |
