Design of an efficient VARMAx model to leverage in-memory computing for faster big data analytics.

The exponential growth of big data has necessitated the development of effective analytical methods for real-time processing of massive amounts of datasets & samples. The design of an effective VARMAx (Vector Autoregressive Moving Average with Exogenous Inputs) model that takes advantage of in-memory computing significantly improves the speed and computational efficiency of big data analytics. This work is required because big data streams must be processed in real-time, as delays can negatively impact business decisions and operations. By anticipatorily processing real-time samples using the VARMAx model, we demonstrate a significant reduction in delay, achieving a 10.5% improvement over recently proposed big data analysis methods. The analysis of sensor data, optimization of the supply chain, and financial forecasting are just a few of the many domains and applications that can benefit greatly from our methodology & operational process. The VARMAx model is a versatile instrument for the analysis of big data because it can handle a variety of data types, including time series, textual, and categorical datasets & samples. Our research also demonstrates the computational efficiency advantage of the VARMAx model over other approaches. By utilizing in-memory computing, which enables faster processing and analysis of large data streams, we achieved a remarkable 8.3% increase in computational efficiency levels. The ability of the VARMAx modeling process to capture the intricate dependencies and relationships present in big data streams justifies its application. Considering both the autoregressive and moving average components as well as exogenous inputs, the VARMAx model successfully captures temporal patterns, seasonality, and the effect of exogenous factors on the datasets & samples. This comprehensive modeling strategy improves the analytics' precision and predictive capabilities, making it the best option for utilizing in-memory computing for large-scale data analysis. [ABSTRACT FROM AUTHOR]