A Deep Learning Framework for Effective Anomaly Detection in Time Series Data

This Study presents a novel method for identifying anomalies in time series datasets by employing deep learning algorithms. Our approach blends autoencoders with Long Short-Term Memory (LSTM) networks. Although we concentrate our experiments on data related to electric vehicles (EVs), our overall objective is to show how flexible our method is for detecting anomalies in a variety of time-based datasets. The Autoencoder's capacity to rebuild input sequences and identify anomalies based on reconstruction mistakes is complementary to the LSTM network's ability to capture complex temporal patterns. By combining these methods, we provide a strong structure that may be employed to determine abnormalities in time series data and support well-informed decision-making in a variety of contexts. The accurate detection of abnormalities by our method improves the dependability of data analysis. Subsequent investigations may investigate additional improvements and expansions of LSTM and Autoencoder models with the goal of finding outliers in time series data.