You have seen various headlines such as “Experts predict a massive spike in Bitcoin’s value” or “Analysts predict a market crash in the coming months”, but how do these experts or analysts predict these ever-changing prices of cryptocurrencies that depends on several factors such as hash rates, mining, demand and supply, government rules and legislations, technological prowess, and so much more. There are various tools, techniques, and strategies that can help in predicting the prices of what particular crypto could demand in the future. Of course, it is impossible to predict the exact price of a coin right now and the best tools can only tell you half the story behind the coin, but right now the advancements and improvements in technologies such as Artifcial Intelligence(AI) and Machine Learning(ML) have boosted the prediction capabilities and the speed with which we can predict the market trends of the cryptocurrencies. Here are fve models that can predict cryptocurrency prices and are considered one of the most sophisticated models for providing near-accurate results.
Moving Average (MA) Model
Moving Average or MA is a model that is also known as the moving-average process and is one of the commonly used time series forecasting models.
In Statistics, a moving average is an estimation to examine main items by making a progression of averages of various subsets of the complete dataset, which includes the historical prices of the said coin. A moving average is normally utilized with time-series information to streamline short-term fuctuations in the trends or cycles. The edge between short-term and long-term relies upon the application, and the boundaries of the moving average will be set appropriately.
For instance, it is generally utilized in specialized examination of stocks, and so it can also be used for forecasting crypto prices which can vary in a short amount of time. SMA, short for Simple Moving Average, works out the average of the scope of closing prices over a particular number of periods in that reach. Exponential MA, or EMA, is different from SMA in the sense that it assigns equal weights to all the historical data points and applies higher weights to the recent prices. The advantage of EMA over SMA is that EMA is more perceptive to price changes, and since the crypto market is highly volatile, it is useful for short-term trading.
Vector Autoregression (VAR) Model
Similar to the MA model, the Vector Autoregression model, short for the VAR model, is also one of the commonly used time series forecasting models. The vector autoregression (VAR) model is a statistical model for capturing the relationship between several variables as they change over time.
Taking the example of Bitcoin (BTC), understanding how BTC is valued and how different things infuence it is one of the most important questions. The BTC market mechanics can be deconstructed using the VAR model and its factors of infuence can be analyzed. This analysis contributes to a comprehensive understanding of what drives BTC and can be useful to a wide range of stakeholders.
Autoregressive Integrated Moving Average (ARIMA) Model
The ARIMA model is a concept of an autoregressive moving average (ARMA) model. Both of these models are used to ft time series data in order to better understand or anticipate future points in the series for forecasting, making it ideal for predictions in the cryptocurrency market. One can predict the price of Bitcoin using the historical data and using that data with the ARIMA model yields an AIC score(Akaike Information Criterion) that shows the compatibility of the model with the data and the complexity of the model.
A model with a large number of features such as opening price, closing price, lowest price, highest price, market capitalization, and the traded volume that match the data will have a higher AIC score than one with the same accuracy but fewer features. There are various languages such as Python and R that can perform Data Science operations and thus help in constructing the model for prediction purposes.
Seasonal Autoregressive Integrated Moving Average (SARIMA) with exogenous factors (SARIMAX) Model
SARIMAX is a more recent version of the ARIMA model. ARIMA incorporates an autoregressive integrated moving average, whereas SARIMAX incorporates seasonal effects and exogenous factors in addition to the autoregressive and moving average components. Exogenous factors are factors such as Technological changes and economic changes, whose values are determined by the model, which in this case is the SARIMAX model. The volatile crypto market that is affected by numerous factors such as market capitalization, economic changes, and government rules and regulations can be termed exogenous
factors. Therefore, the prediction model can be further improved by using SARIMAX.
Recurrent Neural Network and Long Short-Term Memory (LSTM) Model
Recurrent Neural Networks are a sturdy and strong sort of neural organization that is viewed as perhaps the most expert algorithm since they are the only ones with inner memory, making them perfect for handling machine learning problems requiring sequential input. Recurrent neural networks were invented in the 1980s, but their true potential was not realized until recently. The expansion in computational power joined with the huge measures of information we currently need to work with, just as the innovation of momentary memory (LSTM) in the 1990s, has really pushed RNNs to the forefront. For sequential data such as time series, fnancial data, and so on, the algorithm performs admirably.
RNNs can gain a far more profound handle of succession and its setting when contrasted with different algorithms. Long short-term memory networks are a type of RNN that extends the memory. Accordingly, it is appropriate to gain from important insights that have an extremely prolonged stretch of time slacks between them. RNNs can remember inputs for a long time thanks to LSTMs. This is due to the fact that LSTMs store information in a memory, similar to a computer’s memory. LSTMs can perform read, write and delete operations from their memory. Therefore, LSTMs are excellent models for analyzing and predicting time-series information such as cryptocurrency prices.
In conclusion, there is not one single model that can provide you with the most accurate price of a particular coin. Adopting hybrid approaches such as mixing multiple models, cleaning the datasets, and innovating in the existing ones can result in more accurate predictions but right now, these are the ones that can give satisfying results.