An efficient approach for detecting anomalous events in real‐time weather datasets

Abstract

Event detection in real‐time is applied in diverse domains such as detection of fraudulent activities in commercial transactions, detection of faulty systems in industries, and so forth. Businesses and organizations benefit from the actionable information obtained through various techniques available for anomalous event detection. Real‐time event detection is nowadays handled through streaming data frameworks. Traditional approaches effectively handle event detection in real‐time but with more false positives, thus, resulting in false alarms. In this article, an efficient approach comprising two components, an offline model and an online event detection pipeline, is proposed to achieve minimum mean absolute error (MAE). An offline module is developed to investigate a variety of deep learning models that prove suitable for event detection in real‐time. The experiments conducted with PubNub sensors datasets demonstrate that the long short‐term memory unit of recurrent neural networks is the best suitable model for anomalous event detection. The online pipeline module is built using streaming data frameworks to predict the abnormal peaks. It is revealed through the experimental results that the proposed approach efficiently detects anomalous events in real‐time and also eliminates false positives.

Cleaning Big Data Streams: A Systematic Literature Review

“…Data were collected from the urban traffic network of Hague, and the results were compared to those resulting from naïve multivariate long short-term memory in terms of running time. Arora et al [41] suggested a framework to detect global outliers consisting of five stages, namely data collection, data pre-processing, data splitting, optimization and training models, and model evaluation using deep learning, including long short-term memory and a recurrent neural network for the offline mode and deep neural networks for the online mode. PubNub sensor datasets were used for the evaluation, wherein the offline mode was compared among different models and weather sensor data were used to evaluate the online model.…”

Open‐domain event schema induction via weighted attentive hypergraph neural network

“…Event schema induction is an essential step in understanding events to adapt to new domains, and it is widely used in various event tasks, such as event detection, 1 event extraction, 1,2 and causal inference 3 . Previous studies rely on manually predefined event schema and corresponding annotations to learn their model.…”