ItemConference Paper

Deploying a Multi-Model Forecasting System for Bitcoin Prices: Bridging Statistical Forecasting and Deep Learning Innovations

Title
Deploying a Multi-Model Forecasting System for Bitcoin Prices: Bridging Statistical Forecasting and Deep Learning Innovations
Creator
Varalakshmi, Ch.; Ranka, Monica; Christina, Sowmya; Sucharitha, M. Martha; Basha, Md. Shaik Amzad
Description
In this study, we investigate and compare several forecasting models for predicting Bitcoin market prices using historical data sourced from Nasdaq Data Link (formerly Quandl) spanning from 2016 to 2025. Our analysis evaluates traditional time series methods - such as ARIMA and Holt Winters exponential smoothing - alongside modern machine learning and deep learning techniques including LSTM, Prophet, XGBoost, SVR, Random Forest, and GRU. Performance was assessed via metrics such as RMSE, MAE, MAPE, sMAPE, directional accuracy, and R-squared. Our experiments reveal that while classical methods (e.g., ARIMA and Holt Winters) exhibit large estimation errors and limited explanatory capacity, advanced neural network architectures - particularly the GRU - demonstrate superior accuracy with an RMSE of 2,505.84, MAE of 1,760.93, MAPE of 2.79%, and an R-squared of 0.99. The best-performing model (GRU) was deployed as a web application on PythonAnywhere, providing real-time forecasts through an interactive dashboard. This deployment not only validates the predictive efficacy of the GRU model but also offers a practical tool for investors and financial analysts to monitor and predict Bitcoin price movements using reliable Nasdaq data. 2025 IEEE.
Source
3rd International Conference on Data Science and Information System, ICDSIS 2025;
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
bit coin price prediction; data link; deep learning; deployment; nasdaq
Coverage
Varalakshmi C., Andhra Loyola College, Department of MBA, Vijayawada, India; Ranka M., Dayananda Sagar College of Arts Science and Commerce, Bengaluru, India; Christina S., Christ (Deemed to Be University), Department of Professional Studies, Bengaluru, India; Sucharitha M.M., Christ (Deemed to Be University), Department of Professional Studies, Bengaluru, India; Basha M.S.A., GITAM (Deemed to Be University), GITAM School of Business, Hyderabad, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833154265-8;
Format
online
Language
English
Type
Conference paper

Citation

Varalakshmi, Ch.; Ranka, Monica; Christina, Sowmya; Sucharitha, M. Martha; Basha, Md. Shaik Amzad, “Deploying a Multi-Model Forecasting System for Bitcoin Prices: Bridging Statistical Forecasting and Deep Learning Innovations,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/25971.

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