Anomaly Detection in Time Series Data Using Deep Learning
Keywords:
Anomaly identification, sequential data, recurrent neurons, deep learning methods, Machine learning, learning frameworks, preprocessing methodologies, smoothed smoothing, deconstruction methods, numerous models, data collection, lowering computing complexityAbstract
This paper investigates anomaly identification in historical data using advanced deep learning algorithms. Traditional methods of statistics, while useful, frequently fail to capture complex temporal connections. Our research thoroughly assesses the success rate of various deep learning structures for this job, including neural networks with RNNs, LSTMs, and CNNs. To refine the data, optimized preprocessing approaches such as normalization, in addition detrending, as well as the engineering of features is used. The models' adaptability and robustness are demonstrated through empirical validation in a variety of areas, including banking, health care, especially industrial processes. The study emphasizes scalability and processing efficiency to ensure practicality in real-world applications. Furthermore, interpretability methods provide perspectives into the machines' decision-making processes. The results reveal that deep learning models outperform conventional methods, paving the path for improved anomaly identification in time series information. Future study recommendations involve looking into hybrid structures, improving model comprehension, and researching real-time anomaly identification approaches. This work advances anomaly detection algorithms, which could have applications ranging from espionage to maintenance forecasting. The optimized framework offered here has the potential to improve system reliability as well as safety across a wide range of sectors.
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Copyright (c) 2024 Thalakola Syamsundararao, Shobana Gorintla,Erupaka Nitya, R S S Raju Battula, Lavanya Kongala, Amit Verma, Ajmeera Kiran
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