In order to obtain better prediction results, this paper combines improved complete ensemble EMD (ICEEMDAN) and the whale algorithm of multi-objective optimization (MOWOA) to improve the bidirectional gated recurrent unit (BIGRU), which makes full use of original complex stock price time series data and improves the hyperparameters of the BIGRU network. To address the problem that BIGRU cannot make full use of the stationary data, the original sequence data are processed using the ICEEMDAN decomposition algorithm to derive the non-stationary and stationary parts of the data and modeled with the BIGRU and the autoregressive integrated moving average model (ARIMA), respectively. The modeling process introduces a whale algorithm for multi-objective optimization, which improves the probability of finding the best combination of parameter vectors. The ${\mathrm{R}}^2$, MAPE, MSE, MAE, and RMSE values of the BIGRU algorithm, ICEEMDAN-BIGRU algorithm, MOWOA-BIGRU algorithm, and the improved algorithm were compared. An average improvement of 14.4% over the original algorithm’s goodness-of-fit value will greatly improve the accuracy of stock price predictions.

In order to study in depth the differences in basic characteristics between iron ore fines commonly used by a steel company, and guide the sintering performance plant to choose the best ore allocation method, experimental studies on the basic characteristics of seven iron ore powders of three sizes were carried out using micro-sintering equipment, mainly including assimilation properties, liquid phase fluidity, and bonding phase strength. The results of the research showed that with the increase of the iron ore powder particle size, the assimilation of the seven iron ore powders showed an overall decreasing trend, deteriorating fluidity and decreasing bonding phase strength. Among them, the overall fluidity of iron ore powder A was poor, and the fluidity of iron ore powder B varied greatly between different particle grades, and the fluidity of iron ore powder C was more balanced and its bonding phase strength was high, while the overall bonding phase strength of iron ore powders B and E was low. The results of the study provide a theoretical basis for optimal ore allocation in sintering plants.