Cryptocurrency price prediction dataset
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A migrated dataset was created by adding three new columns to the original dataset. The month, day, and year of each data point that was collected was used to calculate average values of the closing price for each of the five tokens. In this case, the following closing prices were examined: three days before the date, one week before the date, and one month before the date. The purpose of this migrated dataset was to ensure that the results of the data were looked at in an efficient way.
Comparing the average closing price between three days versus one month shows that the cryptocurrencies may undergo drastic changes, which is a possible concern that was previously mentioned with people unfamiliar with crypto. Creating these additional columns allowed for a more in depth understanding of the various fluctuations in cryptocurrency when observing the data in a time series model. Regression Analysis The regression model results were evaluated across the five datasets, each having a corresponding Decision Tree regression, linear regression, and Bayesian Ridge regression.
For every token excluding Bitcoin, the data was modeled best by a Bayesian Ridge regression. The Bitcoin dataset performed best with a linear regression model. The results of each regression model were plotted with the predicted and true values of the model. All five graphs show very strong positive correlations, assuring that the metrics of the data fit well in these models. The testing and training models show close values in the charts, as each plotted point is close to the others.
In the Bitcoin graph, the correlation was the strongest. The least number of outliers were present and the predicted values were uniformly plotted against the true values. The most outlier points appeared on the Solana graph, which was not a cause for concern. This could be attributed to the fact that this was the smallest and newest dataset used in this study, therefore there were less data points for the model to use.
Minor fluctuations in price affected this dataset the greatest as the data had only been collected for about a year, but overall, the Solana regression model performed well. Almost all of the R-Squared values are close to 1. This is a great sign for the data, as it explains that the data is not due to random chance and fits these models extremely well. As seen with the XRP crypto, it had a high R-squared value at 0.
Although XRP had the lowest R-Squared value of the five tokens, the overall performance of this token fit best with the model. Taking into consideration each of the three attributes allowed for this conclusion to be made. For the Bitcoin dataset, a linear regression model and attributes best fit the data. The R-Squared value of this model was the closest value to 1, at 0. The high R-Squared value can explain why the Bitcoin model was the most linear token when plotted. The MSE value of Bitcoin was This was due to the fact that the scale of Bitcoin values was much higher than the scale of other tokens.
In this case, a higher value was expected with the higher scale for this token, ensuring that the high MSE was consistent with the data. Overall, there was great stability in the performance of all the data with three regression models. Time Series Analysis The time-series ARIMA model allowed the average closing price of the cryptocurrencies to be examined over the period of time the data was captured. Out of the five tokens, the cryptocurrency that performed the best with the time-series model was Ethereum.
The R-Squared value of Ethereum was the highest, at 0. This R-Squared value is reassuring to the time-series model, as the data needed to be fitted. For the graphical representation of Ethereum, the prediction line was consistent with the testing and training lines. This line did not have much fluctuation from the given prices, which assured that the prediction values were representative of the true values.
The prediction line for Binance Coin had the lowest performance level out of the five tokens. The prediction values strayed from the true values in the chart. The low performance could be attributed to the dataset undergoing major price fluctuations during the most recent price captures. In the first half of closing prices in , the data shows extreme values for the training set. As this was the most recent period of time collected for the model, the prediction line did not have enough information to fully predict if the prices were going to continue to waver.
Result Discussion Overall, the five tokens were fitted well with each model used in this paper. Using regression models to assess the data in order to make new people more confident in investing in cryptocurrency allowed for the element of uncertainty to be diminished in this case. The time-series model allowed the historical data to predict the future prices of cryptocurrency.
Prediction lines proved useful on graphs that captured the moving average values. The positive and strongly correlated results were further explained by the discussion of numeric attribute values. The results of the prediction charts ensure that new investors need not worry about the fluctuation of closing prices, as the cryptocurrencies are able to regulate after extreme trends. There were a few limitations as to what this model was able to produce with data that was collected over a short period of time and that was constantly fluctuating in price.
This could be due to the unusual nature of some cryptocurrencies that experience a sharp incline of investment. This instance is not a fault of the model, but should be examined when reporting historical data. The findings in this paper reiterate the findings from Hua, as the ARIMA model is useful to get an idea of price prediction, but is not entirely accurate in all predictions. Both the regression models and the ARIMA model used historical data to predict future prices of the five cryptocurrencies.
Since the data was scaled evenly and fitted to each model, the results between all the models can be compared. Though this value is a good sign for the model, it was much lower than the R-Squared for regression models using the same data. As discussed, the time-series model did have some disadvantages, but was overall useful to see how future prices of the datasets could be predicted with a moving average.
Both models were able to allow possible new investors to be confident in their investment, as the predictions of each model were consistent with the historical data. The limitations in this study include the number of datasets that were examined. The feelings of people towards a particular cryptocurrency or personality who directly or indirectly endorse a cryptocurrency also result in a huge buying and selling of a particular cryptocurrency, resulting in a change in prices.
In short, buying and selling result in a change in the price of any cryptocurrency, but buying and selling trends depend on many factors. Using machine learning for cryptocurrency price prediction can only work in situations where prices change due to historical prices that people see before buying and selling their cryptocurrency. So, in the section below, I will take you through how you can predict the bitcoin prices which is one of the most popular cryptocurrencies for the next 30 days.
Close Adj Close Volume 0 The AutoTS library in Python is one of the best libraries for time series analysis. Summary Buying and selling result in a change in the price of any cryptocurrency, but buying and selling trends depend on many factors. I hope you liked this article on cryptocurrency price prediction with machine learning using Python.
Feel free to ask your valuable questions in the comments section below.
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