Abstract:
Multivariate analysis and time series modelling are essential data analysis techniques that provide a comprehensive approach for understanding complex datasets and supporting data-driven decision-making. Multivariate analysis involves the simultaneous examination of multiple variables, enabling the exploration of intricate relationships, dependencies, and patterns within the data. Time series modelling, on the other hand, focuses on data evolving over time, facilitating the detection of trends, seasonal patterns, and forecasting future values. In addition to the multivariate and time series analysis techniques, we expand our focus to include machine learning, a field dedicated to developing algorithms and models for data-driven predictions and decisions. The primary contribution of this dissertation is the development of an innovative R Shiny application known as the Advanced Modelling Application (AM application). The AM application revolutionizes multivariate analysis, machine learning, and time series modelling by bridging the gap between complexity and usability. With its intuitive interface and advanced statistical techniques, the application empowers users to explore intricate datasets, discover hidden patterns, and make informed decisions. Interactive visualizations and filtering capabilities enable users to identify correlations, dependencies, and influential factors among multiple variables. Moreover, the integration of machine learning algorithms empowers users to leverage predictive analytics, allowing for the creation of robust models that uncover latent insights within the data and make accurate predictions for informed decision-making. Additionally, the application incorporates state-of-the-art algorithms for time series analysis, simplifying the analysis of temporal patterns, forecasting future trends, and optimizing model parameters. This ground-breaking tool is designed to unlock the full potential of data, enabling users to drive impactful outcomes.
