Testing Data Pipeline Limits for Stock Market Forecasting with Machine Learning Integration

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This research aims to investigate the performance of a data pipeline system implemented using an extract, transform, load (ETL) tool under increased data volume. The system utilizes a data warehouse (DW) for an XGBoost machine learning model to forecast closing stock prices. The data source comes from the Financial Modelling Prep (FMP) API, which provides large amounts of real-time and historical data from the stock market.


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  • Date: 24 Apr 2025
  • Time: 01:30 AM UTC to 02:30 AM UTC
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  • 1000 K.L.O. Rd
  • Kelowna, British Columbia
  • Canada V1Y 4X8
  • Building: HS
  • Room Number: 301
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  • Starts 22 April 2025 06:00 PM UTC
  • Ends 24 April 2025 03:00 AM UTC
  • No Admission Charge




Agenda

6:30 pm - 7:15 pm - Joshua Padron-Uy

7:15 pm - 7:30 pm - Coffee & discussion