Prediction Of Cryptocurrency Prices Using LSTM, SVM And Polynomial
  Regression
Novan Fauzi Al Giffary Feri Sulianta
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