  Forecasting cryptocurrencies as a financial issue is crucial as it provides
investors with possible financial benefits. A small improvement in forecasting
performance can lead to increased profitability; therefore, obtaining a
realistic forecast is very important for investors. Successful forecasting
provides traders with effective buy-or-hold strategies, allowing them to make
more profits. The most important thing in this process is to produce accurate
forecasts suitable for real-life applications. Bitcoin, frequently mentioned
recently due to its volatility and chaotic behavior, has begun to pay great
attention and has become an investment tool, especially during and after the
COVID-19 pandemic. This study provided a comprehensive methodology, including
constructing continuous and trend data using one and seven years periods of
data as inputs and applying machine learning (ML) algorithms to forecast
Bitcoin price movement. A binarization procedure was applied using continuous
data to construct the trend data representing each input feature trend.
Following the related literature, the input features are determined as
technical indicators, google trends, and the number of tweets. Random forest
(RF), K-Nearest neighbor (KNN), Extreme Gradient Boosting (XGBoost-XGB),
Support vector machine (SVM) Naive Bayes (NB), Artificial Neural Networks
(ANN), and Long-Short-Term Memory (LSTM) networks were applied on the selected
features for prediction purposes. This work investigates two main research
questions: i. How does the sample size affect the prediction performance of ML
algorithms? ii. How does the data type affect the prediction performance of ML
algorithms? Accuracy and area under the ROC curve (AUC) values were used to
compare the model performance. A t-test was performed to test the statistical
significance of the prediction results.
