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Prediction models for deep learning from Facebook signal an upward trend in the Bitcoin price by the end of the year

May 8, 2020

A few years ago, when Bitcoin was certainly barely worth $ 15, we would have liked to be able to purchase the cryptocurrency at such an affordable cost. especially since its value would rise to its all-time high of $ 20,000 in 2017.

But how could I have known that beforehand? Could it be predicted? It’s just the dilemma that crypto users face every day, especially now that the long-awaited bitcoin halving is expected.. The event that many people in the ecosystem are full of expectations for, because in its previous three editions it was a driving factor in the price of the cryptocurrency.

Every four years, Bitcoin runs a halving event based on the parameters built into its code. Each halving halves the reward for the mined Bitcoin block.

Prediction models for deep learning from Facebook signal an upward trend in the Bitcoin price by the end of the yearPrediction models for deep learning from Facebook signal an upward trend in the Bitcoin price by the end of the year

Well then How can we solve this price prediction dilemma? Maybe machine learning can give us the answer.

Machine learning sets trends

Machine learning models are likely to give us the information we need to learn about the future of cryptocurrency. It won’t tell us the future, but it could tell us the general trend and the direction in which we expect prices to move. With that in mind, people are trying to take advantage of these machine learning models and predict the future price of Bitcoin.

Machine learning is also referred to as a category of algorithms that software applications can use to predict results more accurately without being explicitly programmed.

Now The basis of this learning is the construction of algorithms that can receive input data and that allow analysis with statistics to predict a result, while being updated as new data becomes available.

And it is the case that the prominent cryptocurrency market, which is characterized by a high degree of speculation and high volatility, represents a new and challenging scenario for the application of forecasting methods to time series. In this context, Bitcoin is characterized by the fact that it covers the largest part of the total market capitalization as well as the total volume of daily transactions.

So there are models for machine learning that are referred to as time series models. These models examine the past and look for patterns and trends to anticipate the future.. Without these models, we would have to do all of these analyzes ourselves, and that would take too long.

Bitcoin time series models and anticipation of the upcoming halving

There are many time series models that can be used to predict the price behavior of Bitcoin. As is the case with Sarima and FB Prophet, the time series models are for learning and experimenting.

It’s almost impossible to predict the future of Bitcoin, but machine learning can help you understand where it could lead with a high level of confidence.. However, it is important to note that using these machine learning models is not necessarily recommended to make all of your investment decisions. But it’s great to see what could happen to Bitcoin and other cryptocurrencies in the future.

Especially within a few days of the long-awaited halving of Bitcoin. A lot is expected of analysts, traders, investors and other enthusiasts of the crypto world.

The next halving will take place on May 12 and traders have mixed opinions about the direction of the BTC price after the event. Some believe a decline will occur, while others expect a bullish continuation. The truth is that what happens after halving is critical to the course of the major cryptocurrency.


Machine learning modeling of SARIMA or ARIMA time series is a relatively basic model This allows historical data from Bitcoin to be collected to determine future behavior.

To achieve this, after modeling, Towards Data Science researchers received a stationary diagram that was adapted to the current conditions of the Bitcoin price by making the data stationary.

The importance of data stationarity eliminates trends in the data set that can be extremely intrusive, so that the model performs better and forecasts more accurately.

Based on the model, it appears that Bitcoin will trend slightly upwards next month and even towards the end of the year instead of falling.

FB Prophet

This other model of the giant Facebook is relatively easier to configure than the previous model. It is an additive model for predicting time series data that is quick and customizable.

According to FB Prophet, Bitcoin will increase next month and possibly towards the end of the current year.

Outside of both models, there are many more on the market today that are used by large ecosystem industries, particularly mutual funds and cryptocurrency exchanges.

Predicting the future of Bitcoin price is almost impossible, but with machine learning we can understand where a high level of trust could go.

Deep learning and machine learning to predict prices

Using neural networks or machine learning models in addition to machine learning to predict bitcoin prices is nothing new, even in traditional markets.

Depending on the problem and the context you want to specify, both tools can work very well in your prediction models. However, the success of one tool or another for something as complex as predicting the future price of an asset depends on the interpretation that the end user gives of the information provided by these algorithms.

There are still many ways to experiment with in-depth and automatic learning in predicting digital asset prices in this regard. mostly because of its high volatility.

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