Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Conference Papers Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Modelling the nexus of macro-economic variables with WTI Crude Oil Price: A Machine Learning Approach Subject The topic of the resource Consumer Preference Index (CPI); Crude Oil Price; Feature Engineering; Forecasting; Gross Domestic Product (GDP); Machine Learning; Predictive Analysis Description An account of the resource Crude oil price shocks have a significant impact on aggregate macroeconomic indices like GDP, interest rates, investment, inflation, unemployment, and currency rates, according to empirical evidence. Various factors like GDP, CPI, and Gold prices show a considerable impact on the Crude old prices. The correlation analysis between these variables can help the machine learning model to find the highly impacting factor of the target variable. The advanced machine learning algorithms can be used to find the most relevant variable impacting the crude oil price followed by predicting the crude oil price. Time series analysis algorithms can forecast the crude oil prices for the specific period ahead. In the current study it was observed that US dollar and CPI show a high impact on Crude oil prices. The study has implemented six machine learning algorithms out of which the ARIMAX was found to be the most efficient model. VAR and ARIMA models are used to successfully forecast the crude oil prices for the next 5 years. From the current research, a machine learning model has been obtained as an outcome of the study, which will help economists in the future to understand the dynamics of crude oil prices driver and forecast it for the near future. 2022 IEEE. Creator An entity primarily responsible for making the resource Bhagat V.; Sharma M.; Saxena A. Source A related resource from which the described resource is derived 2022 IEEE Region 10 Symposium, TENSYMP 2022 Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Date A point or period of time associated with an event in the lifecycle of the resource 2022-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/TENSYMP54529.2022.9864544" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/TENSYMP54529.2022.9864544</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85150819002&amp;doi=10.1109%2FTENSYMP54529.2022.9864544&amp;partnerID=40&amp;md5=a865c6a9d391e5de3a06b80f8fd5cf85" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85150819002&amp;doi=10.1109%2fTENSYMP54529.2022.9864544&amp;partnerID=40&amp;md5=a865c6a9d391e5de3a06b80f8fd5cf85</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 978-166546658-5 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Bhagat V., Christ University, India; Sharma M., Christ University, India; Saxena A., Christ University, India