论文标题
隐藏的马尔可夫模型和LSTM的比较分析：一种模拟方法
Comparative Analysis of the Hidden Markov Model and LSTM: A Simulative Approach
论文作者
论文摘要
时间序列和顺序数据最近引起了人们的重大关注，因为在各个领域的许多实际过程（例如金融，教育，生物学和工程学）可以作为时间序列进行建模。尽管提出了许多算法和方法，例如卡尔曼过滤器，隐藏的马尔可夫模型和长期记忆（LSTM）来对数据进行推断和预测，但它们的用法显着取决于应用程序的应用，问题的类型，可用数据的类型，可用的数据以及足够的准确性或损失。在本文中，我们根据培训，复杂性和预测准确性所需的数据量将受监督和无监督的隐藏模型与LSTM进行了比较。此外，我们提出了各种技术来离散观察结果，并将问题转换为在固定和非平稳情况下的离散隐藏的马尔可夫模型。我们的结果表明，当没有大量标记的数据时，即使是无监督的隐藏的马尔可夫模型也可以胜过LSTM。此外，我们表明，即使不满足一阶马尔可夫假设，隐藏的马尔可夫模型仍然可以是处理序列数据的有效方法。
Time series and sequential data have gained significant attention recently since many real-world processes in various domains such as finance, education, biology, and engineering can be modeled as time series. Although many algorithms and methods such as the Kalman filter, hidden Markov model, and long short term memory (LSTM) are proposed to make inferences and predictions for the data, their usage significantly depends on the application, type of the problem, available data, and sufficient accuracy or loss. In this paper, we compare the supervised and unsupervised hidden Markov model to LSTM in terms of the amount of data needed for training, complexity, and forecasting accuracy. Moreover, we propose various techniques to discretize the observations and convert the problem to a discrete hidden Markov model under stationary and non-stationary situations. Our results indicate that even an unsupervised hidden Markov model can outperform LSTM when a massive amount of labeled data is not available. Furthermore, we show that the hidden Markov model can still be an effective method to process the sequence data even when the first-order Markov assumption is not satisfied.
