Time sequence prediction method and device, equipment and storage medium

The invention relates to an artificial intelligence technology, and discloses a time sequence prediction method, device and equipment and a storage medium, and the method comprises the steps: obtaining a time sequence data set; decomposing the time sequence data set through discrete wavelet transform to obtain a plurality of sub-sequence data; performing feature extraction on the plurality of sub-sequence data to obtain a plurality of feature vectors; inputting each feature vector into a corresponding prediction model for prediction to obtain a prediction result; weighted summation is carried out on the prediction results, a final result is obtained, and the weight of each prediction result is obtained through training of a meta-learning model. According to the invention, the prediction accuracy is improved. 本申请涉及人工智能技术，揭露了一种时间序列预测方法、装置、设备及存储介质，所述方法包括：获取时间序列数据集；通过离散小波变换对所述时间序列数据集进行分解，得到多个子序列数据；分别对多个子序列数据进行特征提取，得到多个特征向量；将各特征向量输入对应的预测模型进行预测，得到预测结果；对各所述预测结果进行加权求和，得到最终结果，其中，各所述预测结果的权重通过元学习模型训练得到。本申请提高了预测的准确率。