Abstract: In order to improve the accuracy of traditional intelligent algorithms in time series prediction and the adaptability of solving engineering data problems， a corrective learning strategy is proposed. Volatility is widely used in the financial field， so it is of great value to predict the volatility of stocks. Since the time series of stock prices are non-linear and non-stationary， predicting the volatility of the stock market has become a difficult point in time series forecasting. In this paper， a simulation experiment is carried out by corrective learning strategy， and a LightGBM-GRU model is designed. Using LightGBM and GRU as the base model and corrector， we predict the volatility of 126 stocks from different industries in the next 10 minutes within 3 years. According to RMSPE，MAE，MSE，RMSE and other indicators： even the classical integrated learning model with good effect， the accuracy and generalization ability also can be improved at the same time by the corrective learning strategy. This paper points out that in the era of algorithm enrichment and big data， the contradiction of intelligent algorithms has turned into a contradiction between the limited versatility of intelligent algorithms and the diversity of engineering problems. Correcting learning strategies can provide new ideas for data simulation.

Key words: corrective learning, stock volatility, time series forecasting, machine learning, neural networks