BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:US/Pacific BEGIN:DAYLIGHT DTSTART:20230312T030000 TZOFFSETFROM:-0800 TZOFFSETTO:-0700 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:PDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20221106T010000 TZOFFSETFROM:-0700 TZOFFSETTO:-0800 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:PST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20221216T004351Z UID:DC19BD04-55EF-4AE8-9709-5966F53CC098 DTSTART;TZID=US/Pacific:20221214T170000 DTEND;TZID=US/Pacific:20221214T180000 DESCRIPTION:Creating accurate predictions in the stock market has always be en a great challenge in finance. With the rise of machine learning as the next level in the forecasting area\, this research compares four machine l earning models and their accuracy for forecasting three well-known stocks traded in the NYSE in the short term over the period from March 2020 to Ma y 2022. We deploy\, develop\, and hypertune XGBoost\, Random Forest\, Mult i-layer Perceptron\, and Support Vector Regression models and report the m odels that produce the highest accuracies from our evaluation metrics: RMS E\, MAPE\, and MPE.\n\nCo-sponsored by: Langara College\n\nSpeaker(s): Ste ven Whang \, Niha Siddikha Sachin\n\nRoom: HS301\, 1000 KLO Rd.\, Kelowna \, Kelowna\, British Columbia\, Canada\, V1Y 4X8\, Virtual: https://events .vtools.ieee.org/m/337514 LOCATION:Room: HS301\, 1000 KLO Rd.\, Kelowna\, Kelowna\, British Columbia\ , Canada\, V1Y 4X8\, Virtual: https://events.vtools.ieee.org/m/337514 ORGANIZER:youry@ieee.org SEQUENCE:2 SUMMARY:Creating Accurate Predictions in the Stock Market URL;VALUE=URI:https://events.vtools.ieee.org/m/337514 X-ALT-DESC:Description: <br /><p>Creating accurate predictions in the stock market has always been a great challenge in finance. With the rise of mac hine learning as the next level in the forecasting area\, this research co mpares four machine learning models and their accuracy for forecasting thr ee well-known stocks traded in the NYSE in the short term over the period from March 2020 to May 2022. We deploy\, develop\, and hypertune XGBoost\, Random Forest\, Multi-layer Perceptron\, and Support Vector Regression mo dels and report the models that produce the highest accuracies from our ev aluation metrics: RMSE\, MAPE\, and MPE.</p> END:VEVENT END:VCALENDAR