Detection of Anomalies in Time Series Data

The detection of Anomaly in time series data has gained a significant amount of popularity due to its indulgence in various domains of industry such as healthcare, the financial sector, industrial services, and other several reasons. This paper represents an elaborate survey of varied anomaly detection techniques that are tested on time series data. The aim of the paper is to identify various anomalous outlines in the dataset through a number of different approaches, algorithms, and techniques. At the beginning of the paper, the importance of detecting anomalies in real-world scenarios is explained followed by describing the efficient techniques capable of dealing with it. The paper covers all the aspects of anomaly detection starting from traditional analytical and statistical models to modern-day advanced machine learning methods. The moving averages, exponential smoothening, and process control which are considered to be the traditional approaches for detecting anomalies in Machine learning-based models such as one-class Support Vector Machine (SVM), deep learning-based models, and isolation forests are the distinct topics that are covered in the paper. Moving ahead, the paper revolves around addressing different challenges and limitations associated with the detection of anomalies in time series data. The challenges circle around topics such as selecting appropriate evaluation metrics, addressing class imbalance, handling high-dimensional data, and how to deal with concept drift. Through a systematic analysis of the existing works, this paper aims to provide data scientists, practitioners, and researchers with summarized information for understanding the background of anomaly detection methods. Finally, this paper delves into a number of methodologies used in advancing anomaly detection in time series data and justifying their impact on real-world applications.