Time series forecasting and anomaly detection using deep learning

Abstract

Recent advances in time series forecasting and anomaly detection have been attributed to the growing popularity of deep learning approaches. Traditional methods, such as rule-based systems and statistical techniques, have limitations when applied to complex and dynamic real-world data. This study investigates using various deep learning models for anomaly detection, recognising aberrant patterns in data, and time series forecasting. The performance of the proposed models is evaluated on benchmarks like the Numenta Anomaly Benchmark (NAB) corpus and credit card fraud detection, showing their ability to detect aberrant patterns in various scenarios. Preprocessing strategies, such as normalisation and feature scaling, play a significant role in both time series forecasting and anomaly detection. In addition, the paper proposes a statistical method for selecting different or more important features from a dataset to overcome the limitations of high-dimensional sequencing data. In many ways, the suggested feature selection technique outperforms previous solutions. It keeps the original meanings of the attributes while selecting those with statistical relevance. Furthermore, it is computationally efficient and successfully solves the problem of excessive dimensions. Overall, deep learning approaches for time series forecasting and anomaly detection are promising in banking, healthcare, and manufacturing industries.