【摘要】 慢特征分析方法(Slow Feature Analysis, SFA)是从已知的非平稳时间序列中提取缓变信息的有效方法。本文首先通过Logistic非平稳时间序列模型对SFA方法提取慢特征信息的能力进行了检验，然后以哈尔滨市为黑龙江省代表站，对月气温距平序列进行慢特征信号提取及预测研究。结果表明：慢特征分析方法可以有效地提取哈尔滨市气温距平序列中的慢特征信号。提取的慢特征信号能够反映原序列的变化趋势、极值等信息。拟合和预测试验表明，与平稳性模型相比，引入SFA信号后的气温预测模型可以在一定程度上提高预测能力，改善预测效果。对近48个月独立样本预测也得到相同结论。更多还原

【Abstract】 Slow feature analysis(SFA)is an effective method to extract slowly varying information from known non-stationary time series.In this paper,the ability of the SFA method to extract slow feature information was first tested by using the Logistic non-stationary time series model,and then the slow feature signals extraction and prediction of monthly temperature anomalies series were carried out at Harbin station as a representative station in Heilongjiang province.The results showed that the SFA method can effectively extract the slow feature signals from the temperature anomalies series in Harbin.The extracted slow feature signals can reflect the trend,extreme value,and other information of the original temperature series.Fitting and prediction experiments show that the temperature prediction model after the introduction of SFA signals can improve the prediction ability,compared with the stationary model.The same conclusion is obtained for the independent sample predictions for the latest 48 months.更多还原