Oct 01, · A more modest prediction was made by the billionaire founder of cryptocurrency firm Galaxy Digital, Mike Novogratz, who forecast that bitcoin would hit the US$40, price level in as Ethereum reached US$1, Bitcoin could be at $40, at the end of Dec 18, UPDATE – As the world’s first and biggest cryptocurrency continues to grow, making a Bitcoin price prediction for is certainly a massive challenge. With Bitcoin having reached a new all-time high of $19, in recent trading, the astonishing ascent of the digital payment method seemingly knows no bounds. Nov 21, · Bitcoin Price Prediction Summary Bitcoin is said to be worth anywhere from $14, to $1,, by industry experts such as Tom Lee, Vinny Lingham and John McAfee. Keep in mind that price predictions are guesses at best, and certainly shouldn’t be taken as financial advice.
2018 prediction of bitcoin priceBitcoin Price Prediction , , , - Long Forecast
This is the dilemma we now face in regards to Cryptocurrency. We do not want to miss out on the next jump in price but we do not know when that will or will not happen. So how can we potentially solve this dilemma? Maybe machine learning can tell us the answer. Machine learning models can likely give us the insight we need to learn about the future of Cryptocurrency. It will not tell us the future but it might tell us the general trend and direction to expect the prices to move.
The machine learning models we are going to implement are called Time Series models. These models will examine the past and look for patterns and trends to anticipate the future. Without these models, we would have to do all of those analyses ourselves and that would take just way too much time. Luckily, we can program these Time Series models in Python to do all of that work for us, which is what we will be doing today!
Facebook Prophet uses an additive model for forecasting time series data that is fast and tunable. These descriptions are very brief and simplified but we will soon go over each step in greater detail. The following code snippets are taken from the Github shared at the end.
The first thing we have to do is retrieve the historical data of Bitcoin which can be downloaded as a convenient CSV file from Yahoo Finance. Then, we use that same DataFrame for the rest of our plotting and calculations. The last two years were selected because Bitcoin, and Cryptocurrency in general became very popular and are a better representation of current market trends.
We do this by simply differencing the data and testing for stationarity by using something called the Dickey-Fuller test. You might be wondering why we care about stationarity. Simply put, stationarity removes trends from the dataset which can be extremely intrusive to our models. Basically, stationarity makes our models perform and predict better. Since we are working with daily data, the ACF shows us which day in the past correlates the most with the current day with respect to the days in between.
PACF shows us which day in the past correlates directly to the current day by ignoring the days in between. In order to get the best performance out of the model, we must find the optimum parameters. We do this by trying many different combinations of the parameters and selecting the one with the relatively lowest AIC score. Depending on your computer, the process of finding the best parameters may take awhile. Unfortunately, not all computers are equal and some models will perform better based on the computer that is running them.
The model tests okay because the actual values still remain within our confidence intervals shaded in gray and the prices are rising as forecasted.
We do this by forecasting from the present day and seeing where it might go in the future. We probably need to take a closer look. According to the model, it appears that Bitcoin will continue slightly upwards in the next month. However, do not take this as a fact. Although, the model seems to be tilting towards the price rising instead of declining.
In the first step, we format our previous data from before by making two columns for the dates and the price. Then, we can jump straight into modeling by fitting and training the data!
No need to tune parameters or check for stationarity! After modeling, we can now advance to forecasting the future by first creating the future dates we want Prophet to predict prices for us. We can also plot these dates which will also show us how the model stacks up against past values and where prices may go next. Zoom in for a closer look at the future forecast. According to FB Prophet, Bitcoin will rise in the next month. But again, this is not a fact.
FB Prophet has even more features and parameters to experiment with, but we did not go through all of them here. Now that we have two forecasts for the future of Bitcoin, feel free to make your own unique observations of both to determine the future of Bitcoin. Do not feel limited to only these two! We just did a brief overview of time series, modeling, and machine learning. There are many more topics to cover and research! See our Reader Terms for details.
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