Predictive Models for Financial Forecasting Using Machine Learning in Cloud Environments

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Dr. Jaspreet Khurana

Waheguru Meher Education Services Pvt. Ltd.

5660 176a St, Surrey, BC V3S 4H1, Canada

Abstract — The growing adoption of cloud-based machine learning has significantly enhanced financial forecasting capabilities, offering enterprises scalable and efficient means to process and analyze vast data sets. This study explores the development and application of predictive models for financial forecasting, leveraging cloud-based machine learning frameworks. We discuss the integration of various algorithms like time series analysis, regression models, and neural networks within cloud ecosystems to predict financial outcomes, such as stock prices, risk assessments, and economic trends. By utilizing real-time data and distributed cloud computing, these models demonstrate increased accuracy and scalability. The results indicate that cloud-enabled predictive models can greatly optimize resource allocation, improve forecasting accuracy, and reduce operational costs. This paper provides a detailed analysis of the methodologies used, their results, and the implications for financial forecasting, emphasizing the transformative role of machine learning in cloud environments.

Keywords

Financial Forecasting, Machine Learning, Cloud Computing, Predictive Models, Time Series Analysis, Neural Networks, Data Analytics.

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