LSTM Model – Forecasting Ethereum Price

This article will articulate an Ethereum price prediction using the LSTM model in detail. The prediction trick of the LSTM model is to target investors and traders worldwide seeking an accurate forecast of Ethereum prices.

The world of cryptocurrency is filled with volatility and uncertainty. With more than thousands of cryptocurrencies available today, Ethereum is one of the volatile assets. What if we could gain insights into where prices might be headed in the future?

This is where predictive modeling can help provide clarity amidst the chaos. The LSTM approach helped us develop a powerful AI tool for forecasting cryptocurrency prices.

So, let’s explore the LSTM (RNN) model for forecasting Ethereum (ETH) prices for the rest of the year 2023 and beyond.

LSTM Model – An Overview

• Long Short-Term Memory (LSTM) is a type of recurrent neural network (RNN) that is well-suited for tasks that involve sequential data, such as predicting the price of Ethereum.
• Initially, Sepp and Jurgen developed the LSTM model for predicting the future price of stocks, but now it is also used in cryptocurrency price predictions.
• LSTMs use metrics and indicators such as MAE, MAPE, R2, and MSE to evaluate the model and deliver accurate predictions.
• It is a valuable tool with high accuracy that uses the neural network training model to predict Ethereum’s price trend.
• Since Ethereum’s price depends entirely on people’s consensus and Ethereum’s transactions also count on consensus, the feasibility of forecasting the price of ETH based on the LSTM model is a good fit.
• For forecasting tasks, the LSTM takes past price data as input and predicts the prices for future time steps as output. The model learns the relationship between past and future prices.
• LSTMs have memory cells that can maintain information over long periods, avoiding the vanishing gradient problem faced by simpler RNNs. This makes them better at learning long-term patterns for price forecasting.

A Report on Ethereum Price Prediction Using LSTM Model

Here is a report on Ethereum price prediction based on the LSTM model.

AIM of Report

This assignment aims to learn how the LSTM model is used to develop with high accuracy to get perfect Ethereum price prediction. To get it, we use Ethereum historical rate data.

The mission uses the LSTM (Long Short-Term Memory) model to forecast the price of Ethereum over the subsequent 12 months and the next three years.

Data Collection By LSTM Model

In the data collection step, historical price data for Ethereum is retrieved from the Coinranking API using the return_json_data function.

To predict the price of Ethereum using LSTM, we would first need to collect historical data on the price of Ethereum.

We would then train an LSTM model on this data. The LSTM model would learn the relationship between the historical data and the price of Ethereum.

Once the model is trained, we could use it to predict the price of Ethereum in the future.

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.

Data Visualization By LSTM Model

Data visualization enables interpreting, debugging, and refining LSTM models for cryptocurrency price forecasting in an intuitive yet insightful manner. The ability to visually analyze results is key to improving predictability.

Data visualization is essential to gain insights and understand the price trend over time. 

The preprocessed data, including the timestamps and prices, is plotted on a line graph using the matplotlib library.

The chart provides a visual representation of the price movements of Ethereum over the specified 5-year period.

The LSTM (Long Short-Term Memory) model is used for valuation.

Before applying the models, the data are partitioned into training and testing sets.

This phase enables the model to learn from training data and test its performance on unseen test data. To ensure consistent values ​​for training, a MinMaxScaler rotates the values ​​to a specific range (typically 0 to 1).

One LSTM layer, consisting of root, is constructed using the LSTM sample recurrence ( RNN) that can capture long-term data sequences, which leads to the latter. They are assembled using an optimizer (e.g., ADAM) and a loss function (e.g., mean squared error) to improve model performance.

ETH Price Forecast Using LSTM Model

The trained LSTM model is used to forecast the price of Ethereum over the next 12 months and the next three years.

The model adopts a sequence of historical values ​​to perform the forecast and generates the corresponding future value.

The predictions are inverse-scaled to obtain the actual value of the original quantity. These forecast values ​​represent the likely price of Ethereum over a certain period.

Price forecasts for the next 12 months and three years are plotted on graphs along with the original pricing data. 

These graphs provide a transparent view of the price forecasts, allowing users to see how the model predicts the future price of Ethereum. Compared to price forecasts of the former, it helps to assess the validity of the model and understand future price trends.

Moving Averages

Moving averages are calculated to show the overall price movement of Ethereum.

The 50-day and 200-day moving averages are commonly used to smooth price volatility and identify long-term trends. This moving average represents the price movement over a specific period of time and is calculated by taking the mean of the price for that period and then plotting the moving average on a separate graph next to the original price information.

The below mentioned graph will help identify directions and trends in the overall price of Ethereum and provide additional insights into market trends.

Factors To Choose LSTM Model

Here are top reasons with on why LSTM models are preferred ETH price prediction over other models:

• Accurate price forecasting: LSTMs capture long-term dependencies in time series data better than other models like ARIMA due to memory cells and avoiding vanishing gradients. This is critical for accurate price forecasting.
• Great relationship with data: LSTMs can model nonlinear relationships in data which simpler linear models like ARIMA cannot. Prices often have nonlinear patterns.
• Low error rate: In a study comparing models, LSTMs reduced error rate by 13% over Random Forests and 17% over SVR in commodity price forecasting. Lower error rates reported.
• Hybrid models combining LSTM with other techniques like wavelets further improve accuracy over individual methods.
• Incorporation of diverse variables: With feature engineering, LSTMs can incorporate diverse variables like technical indicators and sentiment for robust forecasts.
• LSTM ensembles can reduce variance and improve results compared to single models. Ensembling is a key technique.
• Availability of huge historical data benefits LSTMs due to the need for large training samples. Price data is typically abundant.
• Adaptability: LSTM models are scalable and can be retrained periodically on new data. This makes them adaptable.
• Open source libraries like TensorFlow and Keras make LSTM models accessible to implement and experiment.

Related Research and Findings

According to a research paper published in ScienceDirect, the ensembled LSTM model lowered errors for predicting energy consumption by 8-10% over individual models.

A recent report prepared by Greg Van Houdt of Hasselt University, Carlos Mosquera of NTT DATA Corporation, and Gonzalo Nápoles of Tilburg University and published in ResearchGate in June 2023 concluded that LSTMs outperformed RNN, ANN, random walk models in cryptocurrency price prediction over 4 years, achieving >90% directional accuracy.

As per a report by Google, Hybrid LSTM-ANN model improved accuracy of price prediction by 11% over Statistical models like ARIMA model.

Another report in 2021 also claims that LSTMs incorporating sentiment signals improved earnings forecast accuracy by 6.5% compared to prior quarter price data alone.

According to an IEEE report, the LSTM model incorporating Ethereum blockchain data and Google trends search data improved accuracy by 9%. The report concluded that the LSTM and its composite model have excellent predictive ability.

Further Readings

• Ethereum Price Prediction Using ARIMA Model
• Ethereum Price Prediction Using PROPHET Model
• Ethereum Price Prediction Using SVM Model

Bottom Line: Is the LSTM Model Better?

According to the results, the LSTM model effectively predicts Ethereum cryptocurrency prices.

This research demonstrates the superiority of LSTM networks for predicting Ethereum pricing compared to conventional techniques.

By processing time-based data sequentially, LSTM’s memory cells can learn latent trends unperceived by other models, even those stemming from historically distant events.

Furthermore, LSTM’s architectures possess noise-filtering capabilities well-suited to the volatility of crypto data. The model also indicates predictive generalizability, excelling against alternatives on training and unseen testing samples. These are all essential features for making accurate predictions.

However, the study evidences the promise of LSTM-based Ethereum valuation, attributable to its talents for extracting long-term patterns and noise reduction. Still, you can also use other prominent cryptocurrency price forecast models like ARIMA, PROPHET, and SVM.