A Comparative Study: Predicting Time Series Data with Quantum and Classical Machine Learning

This research project embarks on a comprehensive comparative analysis of classical and quantum machine learning models specifically tailored for time series forecasting. By employing a range of classical machine learning models alongside quantum machine learning models, the study aims to predict future data points with heightened accuracy and efficiency. Leveraging AWS Braket, the project facilitates the deployment of quantum models on both quantum simulators and quantum hardware devices, providing a robust platform for empirical evaluation. The goal is to meticulously assess the performance metrics, computational efficiency, and predictive accuracy of these models. Ultimately, the research aspires to enhance the understanding and practical application of quantum machine learning techniques in the realm of time series forecasting, potentially uncovering new paradigms in predictive analytics. Our research indicates that the performance metrics for classical machine learning models yielded superior results compared to those of quantum machine learning models for our use case, although when ensembled with classical models, we get better results.