Prediction Of Cryptocurrency Prices Using LSTM, SVM And Polynomial Regression

Comparative Analysis of LSTM, SVM, and Polynomial Regression for Cryptocurrency Price Prediction

Introduction

The paper presents a rigorous examination of various algorithms intended for the prediction of cryptocurrency prices, a subject of burgeoning interest given the volatile nature of digital currencies. The authors, Novan Fauzi Al Giffary and Feri Sulianta, concentrate their efforts on evaluating Long Short Term Memory (LSTM), Support Vector Machine (SVM), and Polynomial Regression algorithms to determine which method provides the most accurate forecasts. Their research emanates from the necessity to devise reliable predictive models amidst the uncertainties that typify cryptocurrency investments.

Methodological Approach

The implementation of the research was structured around a series of well-defined stages, encompassing data collection, preprocessing, allocation, model design, training, testing, and eventual comparison of results through Mean Square Error (MSE) analysis. Utilizing Python as the programming suite of choice, the study's dataset comprised Bitcoin price information from 2020 to 2024, sourced from the Yahoo Finance portal. Notably, the investigation employed an 80:20 data split ratio for training and testing purposes, respectively, an approach that aligns with standard practices in machine learning research.

Model Exploration and Implementation

LSTM

The LSTM model, a derivative of Recurrent Neural Networks, was recognized for its superior ability to model time series data, making it an apt choice for this investigation. Through meticulous testing across varying epoch counts, the research pinpointed the optimal configuration that minimized MSE values, underscoring the model's potential for accurate price prediction.

SVM

Transitioning to the SVM model, the research delved into both linear and non-linear kernels, exploiting the GridSearchCV method for hyperparameter tuning. This approach facilitated a comprehensive exploration of parameter spaces, ultimately revealing a configuration that achieved remarkably low MSE values, signaling SVM's robustness in forecasting cryptocurrency prices.

Polynomial Regression

Finally, the Polynomial Regression model was scrutinized for its capacity to address non-linear data trends. By adjusting the degree parameter and employing rigorous testing, the study ascertained the model's efficacy relative to the complexity of the cryptocurrency market's dynamics.

Comparative Analysis and Results

The comparative evaluation of the three algorithms unveiled that the SVM model, employing a linear kernel, outperformed its counterparts by registering the lowest MSE value of 0.02. Conversely, Polynomial Regression exhibited the highest MSE, suggesting a relatively inferior predictive capability within the context of this research. These findings are instrumental in endorsing the SVM model's superiority in cryptocurrency price prediction scenarios.

Conclusion and Future Directions

The study conclusively determined the Support Vector Machine's predominance in forecasting cryptocurrency prices with minimal error. It suggests an expansive path for future research, including the incorporation of additional variables such as market sentiment analysis to enhance predictive accuracy. This paper not only contributes significantly to the field by guiding researchers towards algorithms with proven efficacy but also opens the door for further explorations aimed at refining cryptocurrency price prediction models.

The implications of this research extend beyond academic inquiries, offering practical insights for investors, financial analysts, and policymakers interested in the intricacies of cryptocurrency markets. Moreover, it sets a foundation for the development of sophisticated tools that could potentially stabilize the inherently erratic nature of digital asset investments.