用于多元时间序列预测的金字塔隐马尔可夫模型
Pyramidal Hidden Markov Model For Multivariate Time Series Forecasting
AI总结：
本文提出金字塔隐马尔可夫模型PHMM，通过多步HMM提取短随机状态，并以金字塔式堆叠自适应识别长随机状态，从而提升多元时间序列预测的准确性和全面性。
摘要：
隐马尔可夫模型（HMM）能够基于时间序列的当前值和历史值预测其未来值，使其成为处理各类时间序列的强大算法。大量研究探索了利用先进技术改进HMM，从而发展出多种HMM变体。尽管这些研究表明HMM相较于其他先进算法的竞争力有所提升，但很少有研究认识到将多步随机状态纳入其性能的重要性和影响。在本工作中，我们提出了一种金字塔隐马尔可夫模型（PHMM），能够捕获多个多步随机状态。首先，设计了一种多步HMM，用于提取短多步随机状态。接着，基于PHMM提出了一种新颖的时间序列预测结构，该结构利用金字塔式堆叠来自适应地识别长多步随机状态。通过采用这两种方案，我们的模型能够有效处理非平稳和含噪数据，同时建立长期依赖关系，以实现更准确、更全面的预测。在多个多元时间序列数据集上的实验结果有力地证明了我们所提出的PHMM在时间序列预测中相较于竞争性同类方法的优越性能。
英文摘要：
The Hidden Markov Model (HMM) can predict the future value of a time series based on its current and previous values, making it a powerful algorithm for handling various types of time series. Numerous studies have explored the improvement of HMM using advanced techniques, leading to the development of several variations of HMM. Despite these studies indicating the increased competitiveness of HMM compared to other advanced algorithms, few have recognized the significance and impact of incorporating multistep stochastic states into its performance. In this work, we propose a Pyramidal Hidden Markov Model (PHMM) that can capture multiple multistep stochastic states. Initially, a multistep HMM is designed for extracting short multistep stochastic states. Next, a novel time series forecasting structure is proposed based on PHMM, which utilizes pyramid-like stacking to adaptively identify long multistep stochastic states. By employing these two schemes, our model can effectively handle non-stationary and noisy data, while also establishing long-term dependencies for more accurate and comprehensive forecasting. The experimental results on diverse multivariate time series datasets convincingly demonstrate the superior performance of our proposed PHMM compared to its competitive peers in time series forecasting.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
