Prediction Of Cryptocurrency Prices Using LSTM, SVM And Polynomial Regression

ArXiv ID: 2403.03410 “View on arXiv”

Authors: Unknown

Abstract

The rapid development of information technology, especially the Internet, has facilitated users with a quick and easy way to seek information. With these convenience offered by internet services, many individuals who initially invested in gold and precious metals are now shifting into digital investments in form of cryptocurrencies. However, investments in crypto coins are filled with uncertainties and fluctuation in daily basis. This risk posed as significant challenges for coin investors that could result in substantial investment losses. The uncertainty of the value of these crypto coins is a critical issue in the field of coin investment. Forecasting, is one of the methods used to predict the future value of these crypto coins. By utilizing the models of Long Short Term Memory, Support Vector Machine, and Polynomial Regression algorithm for forecasting, a performance comparison is conducted to determine which algorithm model is most suitable for predicting crypto currency prices. The mean square error is employed as a benchmark for the comparison. By applying those three constructed algorithm models, the Support Vector Machine uses a linear kernel to produce the smallest mean square error compared to the Long Short Term Memory and Polynomial Regression algorithm models, with a mean square error value of 0.02. Keywords: Cryptocurrency, Forecasting, Long Short Term Memory, Mean Square Error, Polynomial Regression, Support Vector Machine

Keywords: Cryptocurrency, Long Short Term Memory (LSTM), Support Vector Machine (SVM), Forecasting, Polynomial Regression, Cryptocurrency

Complexity vs Empirical Score

• Math Complexity: 4.0/10
• Empirical Rigor: 6.0/10
• Quadrant: Street Traders
• Why: The paper uses standard machine learning models (LSTM, SVM, Polynomial Regression) with established equations, but lacks deep mathematical derivation or novel theory, while the empirical work involves real data, data splitting, and comparison metrics like MSE.

  flowchart TD
    A["Research Goal: Predict Cryptocurrency Prices"] --> B["Data: Historical Crypto Prices"]
    B --> C["Methodology: Three Algorithms"]
    C --> D["LSTM<br>Long Short-Term Memory"]
    C --> E["SVM<br>Support Vector Machine"]
    C --> F["PR<br>Polynomial Regression"]
    D --> G["MSE Calculation"]
    E --> G
    F --> G
    G --> H{"Performance Comparison"}
    H --> I["Key Finding: SVM produced lowest MSE 0.02"]