论文标题
模糊认知图和隐藏的马尔可夫模型：时间序列分类任务范围内效率的比较分析
Fuzzy Cognitive Maps and Hidden Markov Models: Comparative Analysis of Efficiency within the Confines of the Time Series Classification Task
论文作者
论文摘要
时间序列分类是非常流行的机器学习任务之一。在本文中，我们探讨了隐藏的马尔可夫模型（HMM）进行时间序列分类的应用。我们区分HMM应用的两种模式。第一个为每个类构建单个模型。第二个，其中一个HMM为每个时间序列构建。然后，我们将两种用于分类器结构的方法传输到模糊认知图的域。然后在一系列实验中研究了已确定的四个模型，HMM NN（hmm，每个系列），hmm 1c（hmm，每类），FCM NN和FCM 1C。我们比较了不同模型的性能，并研究了其超参数对时间序列分类精度的影响。经验评估显示了每系列方法的明显优势。结果表明，HMM和FCM之间的选择应取决于数据集。
Time series classification is one of the very popular machine learning tasks. In this paper, we explore the application of Hidden Markov Model (HMM) for time series classification. We distinguish between two modes of HMM application. The first, in which a single model is built for each class. The second, in which one HMM is built for each time series. We then transfer both approaches for classifier construction to the domain of Fuzzy Cognitive Maps. The identified four models, HMM NN (HMM, one per series), HMM 1C (HMM, one per class), FCM NN, and FCM 1C are then studied in a series of experiments. We compare the performance of different models and investigate the impact of their hyperparameters on the time series classification accuracy. The empirical evaluation shows a clear advantage of the one-model-per-series approach. The results show that the choice between HMM and FCM should be dataset-dependent.
