【摘要】 时间序列具有非线性和不稳定性等特点，当前时间序列预测研究面临模型训练参数多、泛化能力差等挑战，其预测精度无法保证.基于此，本文提出一种基于全局最优的麻雀搜索算法（Globally Optimal Sparrow Search Algorithm,GOSSA）和隐马尔可夫模型（Hidden Markov Model, HMM）相融合的时间序列预测模型（GOSSA-HMM）.根据隐马尔可夫模型在模式识别和分类上的优势，对原始数据做差值处理并划分类别属性，以此作为隐马尔可夫模型的输入.采用全局最优的麻雀搜索算法对隐马尔可夫模型的参数进行训练，以解决参数训练过程中存在的收敛速度慢，对初始值设置敏感的问题.将赋予类别属性的差值数据进行分段，利用改进之后的隐马尔可夫模型测算每段序列走势的概率，从与当前数据走势相匹配的过去数据集中定位相同的模型实现预测.通过对山东半岛15个海洋牧场的溶解氧数据进行预测分析，结果表明与当前主要时间序列预测算法相比，GOSSA-HMM训练的参数较少，计算成本较低，具有更好的预测精度和泛化能力.更多还原

【Abstract】 Time series has the characteristics of non-linearity and instability. There exist some deficiencies in the current researches of time series prediction, such as too many training parameters and poor generalization, which leads to its low prediction accuracy. In order to solve the problems, this paper proposes GOSSA-HMM, a prediction model for time series, based on the fusion of global optimal sparrow search algorithm(GOSSA) and hidden Markov model(HMM). By using the advantages of the hidden Markov model in pattern recognition and classification, the original data in time series can be dealt with by the subtractive preprocessing and classification, which is used as the input of the hidden Markov model.The GOSSA is used to train the parameters of the HMM to solve the problems, such as slow convergence speed and being sensitive to the initial value setting. The D-value data endowed with category attributes are to be segmented, the probability of the trend of each sequence will be calculated by using the improved HMM, and the prediction can be achieved by locating the same pattern with the past datasets matching the trend of the current data. The dissolved oxygen data from 15 marine ranches in the Shandong Peninsula are combined for analysis. Under the same experimental conditions, GOSSA-HMM has fewer training parameters, a lower cost of calculation, and better prediction accuracy and generalization.更多还原