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 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 valuation of a coin; but now the advancements and improvements in technologies such as Artificial 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 five models that can predict cryptocurrency prices and are considered the most sophisticated models for providing near-accurate results.
Also read: How to prioritize your crypto-investment
1. Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Model
A Recurrent Neural Network (RNN) is a type of AI network that works with sequential data. These networks have the advantage of having an inner memory that makes them suitable for performing various ML tasks such as price prediction. Recurrent neural networks were invented in the 1980s, but their true potential was not realized until recently. The expansion in computational power combined 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, financial data, and so on, the algorithm performs admirably. RNNs can gain far more success 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 extremely prolonged time slacks between them. RNNs can remember inputs for a long time thanks to LSTMs. This is because of 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.
2. 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 periodic effects and exogenous factors on top of the ARIMA model. Exogenous factors are factors such as technological changes and economic changes, whose values are determined by 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.
3. 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 fit 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 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.
4. 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 influence it is one of the most important questions.
The market mechanics of BTC can be deconstructed using the VAR model and its factors of influence can be analyzed. This analysis contributes to a comprehensive understanding of what drives BTC and can be useful to a wide range of stakeholders.
5. Moving Average (MA) Model
Moving Average or MA is a model which is one of the commonly used time-series forecasting models.
A moving average is a statistical estimation that examines main elements by averaging various subsets of the entire dataset, which includes the past values of the crypto coin in question. A moving average is normally utilized with time-series information to streamline short-term fluctuations in the trends or cycles. The edge (difference) 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 company 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 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.
In conclusion, there isn’t a 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.
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