Enhancing Business Intelligence With Real-Time Data Streams Using Long Short-Term Memory (LSTM) Networks

Abstract

In recent years, Business Intelligence (BI) systems are expected to process and analyze high-volume data streams in real time to assist with timely and data-driven decision-making. Traditional BI architectures, which are heavily dependent on batch-oriented extract-transform-load (ETL) pipelines and fixed reporting dashboards, are not well-suited to today's enterprise data, which is temporal and non-linear. This paper presents an innovative paradigm combining LSTM neural networks with BI pipelines for stream analytics and predictive intelligence in real time. The model architecture proposed uses a multi-layer LSTM network combined with attention mechanisms to learn long-term temporal relationships in various data streams, such as financial, operational and IoT data. A sliding window pre-processing approach is used to preprocess a continuous data stream into a sequence of supervised learning data, which allows online learning and an incremental adaptive model. The predictive accuracy and anomaly detection capability are tested on publicly accessible benchmark data sets, the Numenta Anomaly Benchmark (NAB), the UCI Electricity Load Diagrams dataset, and the financial time-series data set of the S&P 500. Three public benchmark data sets are used to test predictive accuracy, anomaly detection capability, and inference latency: the Numenta Anomaly Benchmark (NAB), the UCI Electricity Load Diagrams dataset, and the financial time-series data set of the S&P 500. The proposed model outperforms the baseline models, such as vanilla LSTM, GRU, Transformer, and statistical models, with an RMSE of 0.034, an anomaly detection F1 score of 94.7%, and a MAPE of 2.31%. Results from an ablation study agree with the independent contribution of each of the architectural components. The framework is shown to have sub-second inference latency, which is ideal for production-grade BI deployments. The results prove that the use of LSTM-augmented BI is an effective and better solution than traditional real-time analytics methods.