ARIMA Model in Ethereum Price Prediction

In this article, we’ll explore Ethereum price prediction using the ARIMA model, one of the top 10 statistical models for analyzing cryptocurrency prices.

Ethereum (ETH / Ether), the world’s second-largest cryptocurrency, has seen its price fluctuate wildly in recent years. With high volatility comes excellent opportunity but also significant risk. Investors are eager to predict where Ethereum’s price may head.

Our chosen ARIMA model makes predicting Ethereum prices completely straightforward and systematic. No more relying on hunches or gut feelings – solid data and reliable patterns back our method.

We analyze historical Ethereum pricing data, choose the optimal ARIMA parameters, fit the model, and use it to generate short and long-term Ethereum price forecasts. The results provide valuable insights into the major factors driving Ethereum’s price trends.

Let’s explore an ARIMA model, a powerful tool used for Ethereum price prediction.

An ARIMA Model – Overview

1. Autoregressive Integrated Moving Average (ARIMA) is a powerful statistical model and forecasting tool for analyzing and forecasting time series data daily.
2. ARIMA rose to prominence in the 1970s, comprising three key elements – autoregressive, moving average, and integration components.
3. It follows three parameters p, d, and q.
4. ARIMA is a flexible, powerful forecasting tool with applications across finance, economics, weather prediction, etc.
5. A significant advantage of ARIMA models is they require only a univariate time series to make predictions, unlike more complex methods.
6. ARIMA model is used to predict the prices of the major cryptocurrencies such as Bitcoin, Ethereum, Dash, and XRP using daily, weekly, monthly, and yearly time series.
7. In daily cryptocurrency price predictions, ARIMA models have recently surpassed other leading price prediction methods for crypto coins, achieving the lowest errors in MAE, MSE, and RMSE.

A Report on Ethereum Price Prediction Using ARIMA Model

Here are some approaches used in the ARIMA model in Ethereum (ETH) price forecast.

Data Collection By ARIMA Model:

In this code, we use the Coin Ranking API to retrieve and store historical price data for Ethereum. By specifying 5 years, we ensure that the data collected covers the main period of analysis and forecasting.

This comprehensive dataset provides a solid foundation for understanding Ethereum’s price trends and making informed predictions. 

The Coin Ranking API allows me to capture accurate and up-to-date pricing information and the Ethereum market’s dynamic characteristics. This historical data is essential for developing predictive models and analyzing cryptocurrency price behavior. Ethereum, for example, historical data is one of the top 10 factors that affect Ethereum prices.

Data Preprocessing By ARIMA Model:

Our initial phase involves wrangling the raw data into a Pandas DataFrame, streamlining analysis while prepping for modeling. It structures the data for flexible manipulation.

We then pull out the timestamp per data point, recording when each Ethereum price was cataloged.

By converting timestamps into date and time objects, we make handling and interpreting the time-related information in the data more accessible.

It is worth mentioning that the data set may have missing values ​​in the value column. To tackle missing values, we implement imputation using the mean. It substitutes nulls in the value column with the average of existing values. Filling gaps with the mean reasonably approximates missing data, yielding a consistent dataset.

To gain insights from Ethereum price trends across geographies, we resampled the data monthly and annually and repeated the data.

This resampling strategy involves collecting data over a specific period, such as taking an average or total value at each interval. 

Resampling the data gives us a summary view of the Ethereum price monthly and yearly. This allows us to analyze and model prices over time.

Monthly Price Prediction Using ARIMA Model:

We trained an ARIMA (Autoregressive Integrated Moving Average) model using monthly data.

After matching the model with the ‘Prices’ column of the resampled monthly data, we got a model that captures the pattern and trend of the Ethereum price over time.

We’ll use this trained ARIMA model to set month 12’s next prediction. To see how the model works and visualize the forecast, we will plot the actual value of the Ethereum price along with the predicted value on a line chart.

This visualization will provide a clear comparison between observed and predicted values.

By looking at the line chart, we can gain insight into the accuracy of the ARIMA model to capture the Ethereum’s future price movement.

Yearly Price Prediction Using ARIMA Model:

Once the ARIMA model was trained and fit, we predicted the next five years. These predictions were based on historical data and observed patterns in the timeline.

The objective was to predict future price trends for Ethereum and gain insight into its potential value in the coming years.

Why We Use ARIMA Model in Ethereum Price Prediction

• Time-dependent capture: The ARIMA model captures patterns and dependencies in time series data, allowing us to understand how Ethereum prices evolve.
• Handling Variables: The ARIMA model effectively handles statistical variables in different contexts, enabling us to analyze and predict Ethereum prices in a dynamic market environment.
• Model flexibility: By combining autoregressive and moving average components, the ARIMA model offers flexibility in capturing complex price dynamics, making it suitable for Ethereum price prediction.
• Future price predictions: The ARIMA model generates accurate predictions of future Ethereum prices based on historical observations, helping us anticipate market trends and make informed investment decisions.
• Interpretable Results: The ARIMA model provides interpretable results, allowing us to gain insights into the impact of past Ethereum prices on current prices, facilitating decision-making and risk management strategies.

What Do Other Research Reports Say?

According to data from Google Trends, global searches for “ARIMA” and “Ethereum” have grown 4x over the past five years (Fig 1.1), reflecting the increasing interest in using ARIMA for crypto forecasting.

Renowned international journal ISecure found ARIMA among the top 5 most accurate models for cryptocurrency price prediction, outperforming RNNs and SVMs in recent benchmarking. Its findings also concluded that ARIMA outperformed most other methods in predicting cryptocurrency prices daily, weekly, and monthly.

Related Reading: Other Best Ethereum Price Forecast Models

• Ethereum Price Prediction Using PROPHET
• Ethereum Price Prediction Using LSTM
• Ethereum Price Prediction Using SVM

Bottom Line

Ethereum is one of the best cryptocurrencies in the crypto marketplace. Of the many approaches to predicting Ethereum prices, ARIMA models are the leading choice. The core strength of ARIMA lies in its elegant simplicity – it requires only historical Ethereum pricing data with no need for exogenous variables.

By detecting patterns in how the time series autocorrelation with its past values and errors, ARIMA models uncover key drivers governing Ethereum’s complex price dynamics. The ability to decompose the time series into trend, seasonal, and residual components provides further insights.

Moreover, ARIMA’s statistical foundation allows for quantifying forecast uncertainty and optimizing model parameters. 

The availability of open-source tools to estimate ARIMA models makes this approach highly accessible. For balancing predictive power, practicality, and ease of use, ARIMA emerges as a leading choice amongst forecasting methods for generating reliable Ethereum price predictions.

While no model can predict prices perfectly, ARIMA’s accuracy and sophistication make it a top contender.