In time series forecasting, a challenging and important task is to realize long-term forecasting that is both accurate and transparent. In this study, we propose a long-term prediction approach by transforming the original numerical data into some meaningful and interpretable entities following the principle of justifiable granularity. The obtained sequences exhibiting sound semantics may have different lengths, which bring some difficulties when carrying out predictions. To equalize these temporal sequences, we propose to adjust their lengths by involving the dynamic time warping (DTW) distance. Two theorems are included to ensure the correctness of the proposed equalization approach. Finally, we exploit hidden Markov models (HMM) to derive the relations existing in the granular time series. A series of experiments using publicly available data are conducted to assess the performance of the proposed prediction method. The comparative analysis demonstrates the performance of the prediction delivered by the proposed model.

中文翻译：

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基于隐马尔可夫模型的粒度时间序列长期预测方法

在时间序列预测中，一项具有挑战性且重要的任务是实现既准确又透明的长期预测。在这项研究中，我们通过遵循合理粒度原则将原始数值数据转换为一些有意义且可解释的实体来提出一种长期预测方法。获得的具有声音语义的序列可能有不同的长度，这给进行预测带来了一些困难。为了均衡这些时间序列，我们建议通过涉及动态时间扭曲（DTW）距离来调整它们的长度。包括两个定理以确保所提出的均衡方法的正确性。最后，我们利用隐马尔可夫模型（HMM）来推导出粒度时间序列中存在的关系。使用公开数据进行了一系列实验，以评估所提出的预测方法的性能。比较分析证明了所提出的模型提供的预测的性能。