Anomaly Detection and Time Series Analysis
Ayush Anand, Durgesh Srivastava, Lekha Rani
Abstract
Anomaly detection and time series analysis are essential techniques in data science, with numerous applications in various domains. Anomaly detection involves identifying patterns in data that deviate from the norm, while time series analysis involves analyzing data that changes over time. Combining these two techniques allows for detecting abnormal patterns in time-varying data, which can be used for various purposes, such as identifying equipment failures, detecting fraud, and predicting future trends. However,s several challenges are associated with anomaly detection and time series analysis, including the complexity of data, the need for accurate labelling, and the difficulty of detecting rare events. This paper reviews different types of anomalies, and the standarmethodsds used for anomaly detection and time series analysis, and the challenges and future directions for this field. We also propose potential solutions for improving the efficient anomaly detection and time series analysis efficiency and accaccuracyvanced algorithms and parallel processing. Ultimately, this paper provides an overview of the current state-of-the-art techniqanomaly detection and time series analysis techniques and highlights the potential for future research in this field.