Title: An Unsupervised Anomaly Detection in Electricity Consumption Using Reinforcement Learning and Time Series Forest Based Framework Show Chinese title

Title: 基于强化学习和时间序列森林框架的电力消耗无监督异常检测

Abstract: Anomaly detection (AD) plays a crucial role in time series applications, primarily because time series data is employed across real-world scenarios. Detecting anomalies poses significant challenges since anomalies take diverse forms making them hard to pinpoint accurately. Previous research has explored different AD models, making specific assumptions with varying sensitivity toward particular anomaly types. To address this issue, we propose a novel model selection for unsupervised AD using a combination of time series forest (TSF) and reinforcement learning (RL) approaches that dynamically chooses an AD technique. Our approach allows for effective AD without explicitly depending on ground truth labels that are often scarce and expensive to obtain. Results from the real-time series dataset demonstrate that the proposed model selection approach outperforms all other AD models in terms of the F1 score metric. For the synthetic dataset, our proposed model surpasses all other AD models except for KNN, with an impressive F1 score of 0.989. The proposed model selection framework also exceeded the performance of GPT-4 when prompted to act as an anomaly detector on the synthetic dataset. Exploring different reward functions revealed that the original reward function in our proposed AD model selection approach yielded the best overall scores. We evaluated the performance of the six AD models on an additional three datasets, having global, local, and clustered anomalies respectively, showing that each AD model exhibited distinct performance depending on the type of anomalies. This emphasizes the significance of our proposed AD model selection framework, maintaining high performance across all datasets, and showcasing superior performance across different anomaly types. Show Chinese abstract

Abstract: 异常检测（AD）在时间序列应用中扮演着至关重要的角色，主要是因为时间序列数据被广泛应用于现实场景中。 检测异常带来了显著的挑战，因为异常具有多种形态，使得准确地定位它们变得困难。 先前的研究已经探索了不同的AD模型，针对特定类型的异常做出了具体假设，但这些假设对异常的敏感性各不相同。 为了解决这个问题，我们提出了一种基于时间序列森林（TSF）和强化学习（RL）方法相结合的新模型选择方法，该方法能够动态选择一种AD技术来进行无监督的AD。 我们的方法能够在不需要显式依赖稀缺且昂贵的地面真实标签的情况下实现有效的AD。 来自实时序列数据集的结果表明，所提出的模型选择方法在F1分数指标上优于所有其他AD模型。 对于合成数据集，我们提出的模型除了KNN外，在所有其他AD模型中表现出色，其F1分数达到了0.989。 提出的模型选择框架在合成数据集上提示作为异常检测器时也超过了GPT-4的表现。 探索不同的奖励函数显示，我们提出的AD模型选择方法中的原始奖励函数产生了最佳的整体分数。 我们在另外三个分别具有全局、局部和聚类异常的数据集上评估了六种AD模型的性能，结果显示每种AD模型在不同类型的异常情况下表现出不同的性能。 这强调了我们提出的AD模型选择框架的重要性，它在所有数据集上都保持了高性能，并在不同类型的异常上展示了优越的性能。