This is an empirical study on predicting Ethereum price using the SVM model, a machine learning model used for time-series analysis.
The article aims to describe the SVM (Support Vector Machine) model to predict the price of Ethereum cryptocurrency. We will also see the future trend analysis of Ethereum price using the Support Vector Machine (SVM) model.
Ethereum (ETH / Ether) is one of the most valuable cryptocurrencies after Bitcoin. The rising profit potential and dynamically changing prices have made predicting the prices of Ethereum an exciting subject of study.
Various studies and reports have found that the accuracy rate of the SVM model was around 93.95% compared to other forecasting models.
Before exploring the ETH price prediction with the SVM model, let’s see a brief overview of the SVM model.
The SVM Model – An Overview
- SVM, or Support Vector Machine, is a supervised learning model for classification and regression tasks.
- The SVM model is backed by a high-level ML algorithm, which is mainly used in Ethereum Price Prediction
- It constructs a hyperplane to maximize the margin between two classes of data.
- It uses a kernel trick to transform data into higher dimensions to find an optimal hyperplane.
- Support vectors define the maximum margin that constructs the hyperplane.
- SVM optimization problem minimizes structural risk to avoid over-fitting with regularization.
- SVM applies kernel functions like linear, polynomial, and radial basis to perform tasks.
- SVM is effective in high-dimensional spaces and is memory efficient.
- Key parameters of SVM are C, kernel type, and kernel parameters like gamma and degree.
- SVM has text and image classification applications, bioinformatics, and handwriting recognition.
- Vapnik and Chervonenkis invented the SVM model in 1964.
- SVM is a discriminative classifier based on the statistical learning theory developed by Vapnik.
Ethereum Price Prediction 2023 – 2033
A Report on Ethereum Price Prediction Using SVM Model
Forecasting accurate future prices of a cryptocurrency is essential in the financial sector.
This report aims to predict the price of Ethereum using the Support Vector Machine (SVM) model. The forecast is based on historical price data obtained from the Coinranking API. The methods used include data preprocessing, SVM model training, and visualization techniques to analyze the data.
Data Collection and Preprocessing:
In the data collection step, historical price data for Ethereum is retrieved from the Coinranking API using the return_json_data function. The API is requested with appropriate parameters to recover data for a specific cryptocurrency (Ethereum) and period (5 years). The API response is in JSON format.
The collected data is first processed to extract the most essential information. Then, preprocessing steps to extract timestamps, values, and dates from the stored data. This information is necessary for detailed analysis and imaging.
Handling Missing Values:
After the collection of information, check if there are any missing values in the dataset. Missing values can arise due to numerous motives, such as incomplete facts or fact collection errors. In this analysis, the ISNA ().sum() function is used to count the number of missing values in the “Price” column of the DataFrame.
If the dataset contains missing values, a common approach is filling them with appropriate ones. In this case, the missing values in the “Price” column are filled with the mean value of the available prices using the filling (data[“Prices”].mean()) function. By filling in the missing values with the mean, the data remains complete and suitable for further analysis.
Visualizing Long-Term ETH Price Trends:
A line plot is constructed to gain insight into the price trend of Ethereum over time.
The line plot shows the change in the price of Ethereum on the y-axis compared to the corresponding dates on the x-axis. Each point in the field represents the price of Ethereum on a specific date.
This visualization helps identify patterns, trends, and changes in the price of Ethereum over a particular period. It provides visual information about historical values and a better understanding of their evolution.
Predicting Future Prices:
The SVM model used in this study is built on the SVR (Support Vector Regression) algorithm.
SVR is a version of SVM used for regression tasks, where the goal is to predict continuous values rather than identify discrete classes. SVM model training uses a training set where the inputs are past values (retrospective period), and the target variables follow values .
The model searches for patterns and relationships in the training data, and he uses it to make predictions. After the SVM model is trained, it is used to predict the price of Ethereum over the next 12 months and three years.
The model takes past price values as inputs and indicates prices based on known patterns. The predicted values are then inverse-calibrated to obtain the actual value values. This is important to convert the predicted values back to the original scale, allowing a meaningful interpretation of the predicted values.
A line plot is used to plot the forecast price to evaluate the performance of the SVM model and monitor the future price trends of Ethereum. The initial cost and a 12-month price forecast for the next three years are also planned.
Factors Affecting Ethereum Prices
Annual Price Breakdown:
The price of Ethereum for each year is plotted using a line graph. This graph gives a high-level view of price trends over different years, allowing longer-term patterns to be identified.
Moving averages are calculated and plotted alongside the original price data. Continuous standards smooth short-term changes and reveal long-term trends in the data.
By visualizing moving averages, one can better understand the overall direction and stability of Ethereum prices.
Why to Choose SVM Model for Forecasting ETH Prices
There are several reasons why SVM models might be a good choice for forecasting ETH prices. The top three reasons are:
First, SVMs can handle nonlinear relationships between features, which is vital for financial data that is often nonlinear.
Second, SVMs can generalize well to new data, which is essential for forecasting because the future is always uncertain.
Third, SVMs are relatively robust to noise in the data, which is important for financial data that can be noisy.
Of course, other machine learning algorithms could also be used for ETH price prediction. However, SVMs offer many advantages that make them a good choice for this task.
Related Research Reports
|A 2023 research paper reported that the SVM method has a high accuracy rate of 96.06%.|
|Researchers of the International Journal of Engineering Applied Sciences and Technology published a report in 2022 and found that the SVM model achieved the lowest error rate of 0.032 for Bitcoin price forecasting compared to other ML models.|
|SVM model attained highest accuracy of 70.3% for next-day cryptocurrency price movement prediction (Sharma et al., 2022)|
|SVM model obtained least MAPEs of 3.02% and 2.96% for daily Ethereum price forecasting using two different kernel functions.|
- Ethereum Price Prediction Using ARIMA Model
- Ethereum Price Prediction Using Prophet Model
- Ethereum Price Prediction Using LSTM Model
In this article, we have discussed the SVM model and its efficacy for predicting the price variation of Ethereum.
As per the findings of various studies, the overall prediction accuracy using the Support Vector Machine model was around 93.95%. Furthermore, its error rate was 0.2545 for MAE, 0.2528 for MSE, and 0.5028 for RMSE, which are low compared to the other forecasting models.
Our study also shows that the SVM model performs well in predicting Ethereum Prices and explaining the high volatility of the recent Ethereum price. Other models like BNN (Bayesian Neural Networks), NB, and GB are also the best options. Still, research says that applying support vector machine models surpasses most other kernel methods when forecasting cryptocurrency prices.
The SVM models achieve superior predictive accuracy across key evaluation metrics like Mean Absolute Error, Mean Squared Error, Root Mean Squared Error, and R-squared.
The price prediction of cryptocurrencies is a highly investigated topic in the market of digital assets. Due to rising profit potential, the forecasting market has become an emerging business. Thus, many investors and traders seek a powerful AI prediction tool to get real-time insights. Please keep visiting our precision page of cryptocurrency for updated information.