Using Machine Learning to Forecast Cryptocurrency Prices
If you just ventured into cryptocurrency trading and are wondering whether there is a way to predict cryptocurrency prices, analysts say there are ways to predict digital currencies. Even though the crypto market is filled with uncertainties, several machine learning algorithms can help make assumptions.
How the Prediction Works
Fluctuating exchange rates and high volatility with unregulated transactions are some risks associated with the trade. As such, scientists, investors and financial analysts seek ways to predict cryptocurrency product prices. Forecasting depends on how the analysts understand the causal relationships and the pricing processes. It involves two approaches. One is to use a cause-and-effect model that defines the relationship between exchange rates and macroeconomic variables like balance of payments, inflation rates and public debts. The second approach involves studying the time series and predicting using processes and past results. The Binary Auto Regressive Tree (BART) approach is one of the best algorithms for time series predictions because it comprises several models. Other machine learning models include random forests, linear models, classification and regression models. With that in mind, some of the site tools for predicting cryptocurrency prices include Trading Beasts, Digital Coin Price, and Wallet Investor X.
Even though macroeconomic factors influence currencies and the stock market, most predictions show that pricing mainly depends on Google search, mass media and social media trends, i.e., the most sought type of cryptocurrency determines their prices. That is why leading cryptos like Bitcoin, Ethereum, and Litecoin represent the biggest market share, while others are less common. “What is Bitcoin?” is one of the top Google searches in the U.S. Such inquiries are likely to rank higher than other types. Accompanied by news volume and news sentiment, Bitcoin’s demand and price consequently increase. The main question by now is how often the predictions can be accurate.
Determining a Machine Learning Model’s Accuracy
Well, machine learning model accuracy in Artificial Intelligence is the most efficient way to evaluate accuracy. The accuracy is represented as a percentage of correct predictions over the total predictions, with 0 implying that the model always gives wrong predictions and a value towards 100 showing more reliability. A model that achieves a higher-than-threshold offline performance is good for use since online performance changes with data changes. Offline labeling is used because online data is usually difficult to label. However, there can never be 100% accuracy in machine learning and something we should not look forward to. Such a value indicates an underlying error in validation and evaluation, possibly a coding bug. Having perfect accuracy is practically impossible. One should consider using classical modeling if the accuracy result is 100%. Besides accuracy, an effective machine learning model relies on training speed. Bitcoin prediction using models like Random Forest and Long Short-term Memory has proven that timing is a significant factor in predicting prices. Neural networks are also effective in predicting prices.
The bottom line is that cryptocurrency prices are determined by public recognition, not just the major classes of financial assets. With different machine learning algorithms, one can estimate possible prices. While achieving 100% accuracy is impossible, it is essential to understand how to apply the metric to effectively use machine learning for predicting cryptocurrency prices.
