计算机工程2025，Vol.51Issue(7)：68-77,10.DOI:10.19678/j.issn.1000-3428.0069160
非平稳时间序列多维隐状态的预测机制
Prediction Mechanism Based on Multi-dimensional Hidden States for Non-stationary Time Series
摘要
Abstract
Time series predicting is widely applicable in industrial production,financial decision-making,and early disaster warning.However,most existing methods primarily target stationary time series,failing to accurately capture the evolutionary characteristics of nonstationary sequences.Current approaches for nonstationary time series also inadequately extract multidimensional features and lack comprehensive dynamic perception,thereby compromising prediction accuracy.This study proposes a novel prediction mechanism for nonstationary time series to address these limitations.First,it models seasonality,local trends,and long-term trends that affect sequence stationarity to extract multidimensional hidden states.This study combines the forward-backward algorithm with Maximum Likelihood Estimation(MLE)to compute the maximum transition probabilities for state prediction.Because the mechanism incorporates multiple potential nonlinear factors influencing nonstationary sequences and calculating transition probabilities through a global state perception,it significantly improves the prediction accuracy.The effectiveness of the proposed mechanism is demonstrated through various case studies.Ablation experiments conducted on nine nonstationary time series datasets from diverse domains validate the contribution of each component to the overall prediction accuracy.Comparative results show that both the Mean Absolute Percentage Error(MAPE)and the Root Mean Square Error(RMSE)of the mechanism are consistently lower than those of baseline methods,with the Legates-McCabe index approaches 1 on financial datasets,thereby confirming its robustness and accuracy.关键词
特征提取/状态转移链/时间序列预测/非平稳时间序列/隐状态Key words
feature extraction/state transition chain/time series prediction/non-stationary time series/hidden state分类
信息技术与安全科学引用本文复制引用
张潇,李德识..非平稳时间序列多维隐状态的预测机制[J].计算机工程,2025,51(7):68-77,10.基金项目
国家自然科学基金(62101389). （62101389）
