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CATEGORIES:College of Engineering,Thesis/Dissertations
DESCRIPTION:College of EngineeringData Science Master's Thesis Defense听"Ev
 aluating the Impact of Data Drift on Deep Learning Models for Bitcoin Pric
 e Forecasting"听By Adithi Madduluri听Advisor: 听Dr. Donghui Yan, Mathemati
 cs, UMass Dartmouth听Committee Members:Dr. Yuchou Chang, Computer and Info
 rmation Science Department, UMass DartmouthDr. Long Jiao, Computer and Inf
 ormation Science Department, UMass Dartmouth听Thursday, April 29, 202610:3
 0 am to 11:30 am听Via Zoom: Please contact Adithi Madduluri (amadduluri@um
 assd.edu) or Dr. Yan (dyan@umassd.edu) for the zoom link and passcode听听A
 bstract: Cryptocurrency markets are non-stationary, making price forecasti
 ng inherently unreliable over time. This study examines whether the choice
  of target variable has more impact on forecast stability than the choice 
 of model architecture. Five models are evaluated across two target formula
 tions: raw Bitcoin price and 1-hour percentage change. The models tested a
 re Naive Forecast, ARIMA, LSTM, Bidirectional LSTM, and GRU, each trained 
 and assessed over a 27-day test window using live data collected at 5-minu
 te intervals across a rolling six-week period. Drift detection using the W
 asserstein distance confirmed that raw price exhibits significantly greate
 r distributional shift than percentage change over the same timeframe. Mod
 els trained on raw price produced directional accuracy below 50% across al
 l learned architectures, with visible degradation over the test window. Th
 e Naive Forecast outperformed all learned models on both RMSE ($110.34) an
 d MAE ($66.08). Models trained on percentage change maintained substantial
 ly higher accuracy: LSTM achieved 74.7% directional accuracy, while BiLSTM
  and GRU both reached 72.8%, with no comparable decay observed. The result
 s indicate that model decay in Bitcoin forecasting is driven primarily by 
 data drift in the target variable rather than by limitations in the predic
 ting architecture. When the target is stationary, all models tested retain
  their accuracy across the full evaluation window.听All Data Science and C
 omputer Science Graduate Students are encouraged to attend.听For more info
 rmation, please contact Dr. Donghui Yan at dyan@umassd.edu.\nEvent page: h
 ttps://www.umassd.edu/events/cms/evaluating-the-impact-of-data-drift-on-de
 ep-learning-models-for-bitcoin-price-forecasting.php
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</script><p>College of Engineering<br />Dat
 a Science Master's Thesis Defense<br />听<br />"Evaluating the Impact of D
 ata Drift on Deep Learning Models for Bitcoin Price Forecasting"<br />听<b
 r />By Adithi Madduluri<br />听<br />Advisor: 听<br />Dr. Donghui Yan\, Ma
 thematics\, UMass Dartmouth<br />听<br />Committee Members:<br />Dr. Yucho
 u Chang\, Computer and Information Science Department\, UMass Dartmouth<br
  />Dr. Long Jiao\, Computer and Information Science Department\, UMass Dar
 tmouth<br />听<br />Thursday\, April 29\, 2026<br />10:30 am to 11:30 am<b
 r />听<br />Via Zoom: Please contact Adithi Madduluri (amadduluri@umassd.e
 du) or Dr. Yan (dyan@umassd.edu) for the zoom link and passcode听<br />听<
 br />Abstract: Cryptocurrency markets are non-stationary\, making price fo
 recasting inherently unreliable over time. This study examines whether the
  choice of target variable has more impact on forecast stability than the 
 choice of model architecture. Five models are evaluated across two target 
 formulations: raw Bitcoin price and 1-hour percentage change. The models t
 ested are Naive Forecast\, ARIMA\, LSTM\, Bidirectional LSTM\, and GRU\, e
 ach trained and assessed over a 27-day test window using live data collect
 ed at 5-minute intervals across a rolling six-week period. Drift detection
  using the Wasserstein distance confirmed that raw price exhibits signific
 antly greater distributional shift than percentage change over the same ti
 meframe. Models trained on raw price produced directional accuracy below 5
 0% across all learned architectures\, with visible degradation over the te
 st window. The Naive Forecast outperformed all learned models on both RMSE
  ($110.34) and MAE ($66.08). Models trained on percentage change maintaine
 d substantially higher accuracy: LSTM achieved 74.7% directional accuracy\
 , while BiLSTM and GRU both reached 72.8%\, with no comparable decay obser
 ved. The results indicate that model decay in Bitcoin forecasting is drive
 n primarily by data drift in the target variable rather than by limitation
 s in the predicting architecture. When the target is stationary\, all mode
 ls tested retain their accuracy across the full evaluation window.<br />听
 <br />All Data Science and Computer Science Graduate Students are encourag
 ed to attend.<br />听<br />For more information\, please contact Dr. Dongh
 ui Yan at dyan@umassd.edu.</p><p>Event page: </a></p></body></html>
DTSTAMP:20260423T140200
DTSTART;TZID=America/New_York:20260429T103000
DTEND;TZID=America/New_York:20260429T113000
LOCATION:via Zoom: please contact Adithi Madduluri (amadduluri@umassd.edu) 
 or Dr. Yan (dyan@umassd.edu) for the zoom link and passcode
SUMMARY;LANGUAGE=en-us:"Evaluating the Impact of Data Drift on Deep Learnin
 g Models for Bitcoin Price Forecasting"
UID:37416796251e4b945ddad42a2de6fd78@www.umassd.edu
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