Long-Short Term Memory Method for Blockchain Ethereum’s Market: The Establishment of ETH 2.0 Merger
Firmansyah Yunialfi Alfian
a, Faurani Santi Singagerda
a,1*, Riko Herwanto
ba Department of Business Digital, Faculty of Economics and Business, Darmajaya Institute Informatics and Business, Lampung ,
bFaculty of Computer Science, Darmajaya Institute Informatics and Business, Lampung
* corresponding author
I. Introduction
Cryptocurrency is a digital or virtual currency designed as a medium of exchange. It is a method of creating virtual "coins" and providing them as well as securing ownership of information (encryption) through easily verified cryptographic designs, but it is algorithmically difficult to find a solution in a format that cannot be read and can only be described by someone who has the secret key [1][2]. Furthermore, Terra Name Service (TNS)1, a world-leading research firm, has revealed that 63 percent of Indonesians are familiarized with cryptocurrency. Indonesia outperforms Malaysia, France, Italy, and Romania in terms of cryptocurrency acceptance. According to a research by the International Decentralized Association of Cryptocurrency and Blockchain (IDACB), Jakarta is one of the world's top ten crypto-high capitals.
Cryptocurrency is created as an alternative investment; investment is one of the positive effects of current technological advances; by investing, humans can prepare financially for the future.
According to inner ID marketing research institutions, cryptocurrency ownership vastly outnumber ownership of other investment instruments, such as property, mutual funds, and decentralized stocks [3][4].
Ethereum is the best cryptocurrency in the world, with a peer-to-peer network focused on launching code programs that are decentralized with digital money recognized as Ether [5][6][7][8].
Moreover, the primary benefit of Ethereum is the ability to create smart contracts and decentralized applications, or dapps. Since Ethereum does not have a holding period, it may be traded every day.
1domain reseller platforms may transfer or change ownership by simply entering the web domain and enabling other data features such as NFT, email, Twitter accounts, and others.
ARTICLE INFO A B S T R A C T
Article history:
Received 31 Ags 2022 Revised 6 Sept 2022 Accepted 13 Okt 2022
Objective of research to identify the application of LSTM on Ethereum predictions based on blockchain data and information.
The study is an experimental study using the Long Short Term Memory (LSTM) method to predict blockchain information on the Ethereum market. The method is a development of Recurrent Neural Network (RNN) and Artificial Neural Network (ANN), and required several precise parameters to produce accurate predictions.
The study analyzed several parameters such as the number of neurons in the hidden layer and the most appropriate max epoch to use. The results of the analysis show that using neurons 50 and max epoch 500 are able to predict ethereum prices using blockchain information well, seen from a very small error, namely MAPE of 1.69% with the highest price predictions occurring throughout the middle of 2021 as an effect of changes in the technology system, which is used which previously applied proof of work (mining) to proof of stake (validator) on the current ETH 2.0 technology, as a result there was a decrease in the supply of coins that was currently happening.
Copyright © 2022 International Journal of Artificial Intelegence Research.
All rights reserved.
Keywords:
Blockchain, Cryptocurrency, Fintech, Prediction, Merger
With something like this, Ether holders may conduct transactions whenever and wherever they choose, because all Ethereum transactions are recorded on the blockchain [7][8].
Cryptocurrency is a type of alternative investment that allows for high-yielding returns while also presenting a high risk of loss. As a result, accurate information is essential for any individual or business owner seeking to reduce risk and maximize profits. One method that may be used is to forecast the price of Ethereum in real time [5].
Long Short-Term Memory (LSTM) is a data storage system that can analyze, predict, and categorize information that has been held for a long period. The approach is one of the most common as well as being a modified variation of the recurrent neural network or RNN. According to [9], the method is a development of Recurrent Neural Network (RNN), and RNN is one form of Artificial Neural Network (ANN). ANN algorithms are commonly used in forecasting future prices [10][11], and they're a type of information management system that is implemented using a computer software that can manage a number of different processes while the learning process is initiated.
Meanwhile, [12] characterized RNN as a type of ANN architect designed particularly to analyze continuous or sequential data. RNN does not eliminate old information during the learning process;
this is what separates RNN from regular ANN; RNN is specifically designed to analyze continuous or sequential input [13][14].
[15][16] discovered that the Long Short-Term Memory (LSTM) architecture for data problems is stored for an unusually long time on their study. The design can preserve and update the memory cell state by filtering information through the gate structure [17]. Input, forget, and output gates are all part of the gate structure (each memory cell has three sigmoid layers and one ground layer).
Previously, [18] found that the LSTM model outperformed other techniques by producing lower RMSE and MAPE values. The RMSE value is 0.0702, while the MAPE value is 0.0535 percent.
[19] determined a selection of the most significant Blockchain data contained in Bitcoin supply and demand; and applied them to train models to enhance prediction performance on their research.
An empirical investigation was carried out and the results to compare Bayesian neural networks to other linear and non-linear benchmark models for modelling and forecasting Bitcoin operations [2].
Analytical investigations reveal that the BNN2 coin predicts the price time series effectively and explains the current price volatility.
The study conducted by [20] recommended modifying the present univariate approach of time series classification model, regardless of the fact that the multivariate method of classification has lately gained a lot of attention. The LSTM-FCN and ALSTM-FCN techniques are multivariate approaches with time series classification techniques that integrate complete convolutional blocks with block pressure and excitation to increase accuracy. The proposed model performs many models while requiring minimal pre-processing. It demonstrates that the proposed model will perform well in a variety of job categories by using complicated multivariate time series categorization such as activity, or action recognition [21].
[22] conducted a research involving clinical medical data, specifically in the intensive care unit (ICU) at Children's Hospital Los Angeles in the United States, where the data was multivariate with a sequence of decentralized observations. While data has the potential to hold a multitude of insights, it is challenging to mine efficiently due to varied durations, irregular sampling, and missing data. RNN, particularly LSTM, is a powerful and frequently used learning model for sequence data [22][23]. Both techniques effectively explained the various sequence lengths and distant dependency captures. They provide the first research to objectively examine the capacity of LSTM to recognize patterns in a time series of multivariate clinical measurements. The study focused on the multilabel diagnostic and statistical manual, building a model to classify 128 illnesses from 13 samples of frequent but irregular clinical data.
Cryptocurrency is a decentralized digital or virtual currency [3][6]. Since cryptography is used for security, it is difficult to repair. Several of the popular Cryptocurrencies are Bitcoin, Ethereum,
2 Cryptocurrency coin BrokerNekoNetwork is a new cryptocurrency that uses blockchain technology. The function is to help traders and investors by providing a secure and transparent trading platform. BrokerNekoNetwork (BNN) is a cryptocurrency that operates on the Ethereum platform.
Ripple, Bitcoin money, Bit connect, Dash, Ethereum Classic, Iota, Litecoin, Monero, Nem, Luna, Neo, Numeraire, Stratis, Wave, and so on. Cryptocurrencies grown in popularity in 2013 and have since seen a huge amount of transactions, resulting in unpredictable and volatile pricing. [24] [25]
used machine learning to forecast the price of cryptocurrency by improving the ADAM method [26], and LTSM, which has been shown to be quite efficient in predicting the price of the digital currency [17].
[27] [28], for instance, integrate an artificial neural network technique with a data system architecture based on Long Short-Term Memory Neural Networks. To obtain accurate prediction results, this approach requires the correct parameters. Analyses of numerous factors, including the number of time series patterns, the number of hidden neurons, the number of max epochs, and the composition of the training and test data, on the accuracy of the predictions obtained [28][29]. The research results showed that the system developed is capable of accurately predicting Bitcoin prices, with an average accuracy rate of 93.5 percent for the data examined.
The research of [30] [31] predicted the stock market due to changes in the national economy in China by using the Bayesian-LSTM approach which is modified by using six macroeconomic indicators. The results of the study found that there were market changes in different economic cycles. Using a modified Bayesian-LSTM technique and six macroeconomic indices, the researcher forecasts the static and dynamic stock market in China as a result of changes in the national economy. The study's findings revealed that there are market shifts in different economic cycles by more precisely anticipating stock prices and assisting investors and businesses in making more profitable decisions.
II. Method
This research is an experimental study, which is analysis undertaken to determine the implications of anything done on purpose by researchers [31][32]. In general, the objective of study is to explore or determine the impact of a certain activity on a specific group. The findings were then compared to those of other groups who received other measures. As a result, experimental research is typically conducted to demonstrate a causal relationship among variables or factors [32].
Secondary data was collected from https://etherchain.org/ to collect data information from the Ethereum blockchain and information analysis from https://www.coindesk.com/and https://www.investing.com/ to obtain 2 years of daily Ethereum price data. The study's population consists of all Ethereum pricing and Ethereum blockchain evidence gathered from Ether-chain.
While the samples in this study include Ethereum / USD pricing, Block Size, Average Block, Block Time, Hash Rate, Difficulty, Fee Mining Reward, Mining Revenue, Total Account, and Transaction;
each variable is taken every month for two years from April 2019 to May 2022.
Fig. 1. Flowchart System Source: [27][35]
This study utilized ten variables: Ethereum USD, Ethereum USD, Block Size, Average Block Usage, Block Time, Hash Rate, Difficulty, Fee Mining Reward, Mining Revenue, Total Account, and Transaction. While the methodology applied is Multivariate LSTM, the procedures include categorizing the data into training and testing data, identifying the number of neurons in the hidden layer, determining the number of epochs, and creating forecasts. The predictive data is then de- normalized, which compares actual the error values, particularly MSE, RMSE, and MAPE.
It is fundamentally important to select sequence learning features in the LSTM. There are at least four types of stock data such as (1) historical stock price data (e.g. volume, high, low, open); (2) technical analysis data calculated from (1) (e.g. moving average convergence/divergence (MACD));
(3) historical price data for market index and/or other related shares; (4) economic fundamentals (e.g. gross domestic product (GDP), inflation, exchange rate, and other indicators).
Our sequence learning feature uses Ethereum training and testing data. We found that the learning power of the LSTM sequence would find us the best parameters. In the current study, the order used is the daily index price, maximum and minimum prices.
III. Result and Discussion
Figure 2 illustrated the Ethereum price chart for the previous two years; the graph indicates that the data has been increasing since 2019, with the price of Ethereum reaching at 09 November 2021 at 4808,34 and then declining in early 2022. The growth in the price of Ethereum in early 2021 was quite significant because to the enthusiasm of Initial Coin Offering (ICO), which is a concept of cryptocurrency fundraising, but it dropped in early 2022, even though the market is bearish. [33].
The reason of the cryptocurrency's decline appears to be a massive sell-off by investors in response to escalating inflation concerns. Investors are also continuing to avoid risky assets, which reflects on the stock market. According to [33], one of the factors driving the decrease in crypto prices is investors' declining risk appetite, that puts them fearful of risky assets. With all of its uncertainty and volatility, cryptocurrencies are considered as one of the most volatile investment instruments [1][27][35].
Fig. 2. Evolution of Ethereum Price Source: [36]
Long short-term memory network (LSTM) is a data storage system that can analyze, predict, and classify long-term stored information[15][16]. In the case of predicting (forecasting), the main goal of LSTM is to create accurate predictions of a variable[20]. The prediction error rate is used to determine the best forecasting approach; the lower the error rate generated, the more accurate a method is in projecting. The first step in the LSTM analysis process is to normalize the dataset in order to minimize errors. The real data is then converted into values with a range of 0 to 1 using the min-max scaling approach. The following is an example of applying the initial data to the price variable to determine the minimum-maximum scaler [36][37][38].
Table 1. shows the results of data normalization of variables X1 - X5
No X1 X2 X3 X4 X5
1 0.891230 0.827616 0.977680 0.370903 0.413538
2 0.859704 0.850116 0.947660 0.380347 0.423363
⫶ ⫶ ⫶ ⫶ ⫶ ⫶
728 0.038328 0.208030 0.021381 0.026704 0.362830
729 0.039094 0.290556 0.206651 0.000000 0.375784
a. Source: [19][24][25][36] [37][38]
Table 2. Variable Data Normalization Results X6 - X10, and Y
No X6 X7 X8 X9 X10 Y
1 0.152949 0.494888 0.843330 0.000000 0.934044 0.859704
2 0.155272 0.520897 0.902408 0.004694 0.977572 1.000000
⫶ ⫶ ⫶ ⫶ ⫶ ⫶ ⫶
728 0.172244 0.011839 0.016801 0.998291 0.168457 0.039094
729 0.166797 0.028341 0.019523 0.999398 0.279390 0.045370
b. Source: [19][24][25][36] [37][38]
The normalization results are generated as follows from the findings of the min-max scaler computation using the initial data on the price variable. Furthermore, the data that is examined is divided into training and testing data, where in this study the researcher applies a comparison of 80% for training data and 20% for testing data, which means that there are 583 data for training and 146 data for testing, resulting in the following outcomes on table 3
Table 3. Data Normalization Results for X6 - X10 & Y
Unit Data Training Data Testing
Percentage 80 % 20 %
Total data 583 146
c. Source: [19][24][25][36][37][38]
Providing training data is required to encourage LSTM performance on data testing; training is more essential than data testing because machine learning or learning algorithms are better trained with data patterns from testing phase. This is important when an algorithm or machine creates a model and the applying this technology to the test data predicts the testing data accurately [17][26][38]. The supplied machine learning model will be used in the training process. The LSTM method is used during the training phase to create a model that will ultimately be assessed for performance against the testing results [15][20][38]. The procedure is continued until the most accurate model is generated. The model will then be utilized for prediction after finding the best model.
In this study, a network with 10 input variables and 1 output layer will be adopted for the experiment, with the number of neurons in the hidden layer being 10, 20, 30, and 40, and the experiment on epochs will be carried out with epochs of 100, 500, 1000, and 1500. The number of neurons and the more precise epoch, indicated by the smallest error value created, will be known in the experiment, and the researcher will also look at the graph of the prediction results and the actual data to see if they follow each other or not, applying ADAM optimization[26][39].
Table 4. Neutron and Epoch Experiment Results No Total Neuron Epoch Error (MSE)
1
10
100 1625.887
2 500 71.694
3 1000 180.757
4
20
100 4959.099
5 500 62.183
6 1000 49.014
7
30
100 1302.105
8 500 62.909
9 1000 73.327
10
40
100 2865.294
11 500 81.461
12 1000 60.642
13
50
100 2347.884
14 500 45.449
15 1000 48.350
d. Source: [17][26] [27][28] [36]
Table 4 revealed that the maximum accuracy on the number of neurons and epochs is at number 14, specifically by employing 50 neurons and 500 epochs, with the lowest error value of 45,449. The number of epochs (iterations) reflects the length of the learning process on the network under consideration. According to [11][12], overly few epochs result in a network that is too generic, which means that the network's capacity to identify patterns is limited, if not non-existent. While a large number of epochs will cause the network to overfit (the network is too sensitive to the training data), it can be shown in table 4 that the best results are not in the highest epoch value, but neurons and 500 epochs, and this architecture will be implemented in Ethereum price prediction. Figures 2 and 3 illustrate the weight and bias values estimated from neurons 50 and epoch 500, respectively.
Figure 2 shows the weight formed by neuron 50 and epoch 500, where w i is the input weight, w f is the forget weight, w c is the cell weight, and w o is the output weight. The weight value or weight is derived from random results. Menawhile, figure 3 shows the bias created by neurons 50 at epoch 500, where b i represents the input bias, b c represents the cell bias, b f represents the forget bias, and b o represents the output bias. The weight and bias value are on the forget gate, input gate, and output gate, respectively. The information on each input data will be processed at the forget gate, and which data will be saved or discarded in memory cells, the input gate is the input value, and the output gate is the output, and all activities in the LSTM cell have a bias and weight.
Fig. 3. The result of the weight of each neuron for the Variable Price Source: [9][12][17][26] [36]
Fig. 4. Bias for each Neuron Source: [9][12][17][26][36][39]
After determining the optimum architecture for producing predictions, we receive a graph comparing actual Ethereum price data with Ethereum price prediction data, as shown in Fig.4.
Fig. 5. Actual and predicted data graph Source: [17][24][25][26] [36]
Thus, the Ethereum price prediction results may be obtained from the denormalization findings as follows:
Table 5. Prediction Results
No Date ETH Price Predictions Actual Data
1 15 June 2021 2.527,297 2.581,42
2 16 June 2021 2.371,597 2.544,35
3 17 June 2021 2.322,928 2.367,78
4 18 June 2021 2.164,327 2.372,10
5 19 June 2021 2.174,529 2.229,53
6 20 June 2021 2.066,425 2.166,30
7 21 June 2021 1.904,275 2.244,25
: : : :
117 07 November 2021: 4.503,153 4.517,27
118 08 November 2021 4.612,775 4.612,05
119 09 November 2021: 4.715,847 4.808,34
: : : :
140 1 December 2021 4.583,28 4.538,5772
141 2 December 2021 4.512,90 4.447,6023
142 3 December 2021 4.219,30 4.078,4922
143 4 December 2021 4.123,46 3.569,9379
144 5 December 2021 4.194,80 4.047,0763
145 6 December 2021 4.353,31 3.946,2604
146 7 December 2021 4.307,08 4.268,0829
Source: [24][25][26] [35] [36]
Table 6 presents the price prediction results for Ethereum, with an accuracy of 98.34 percent based on the Mean Absolute Percentage Error (MAPE) value. With a MAPE score of 1.69 percent, the prediction results are remarkable [23]. Since the evolution in ETH 2.0 technology over the last two years, the role of proof of work (mining) has shifted to that of proof of stake (validator), resulting in a decrease in supply of coins that really can keep up with demand in the current Ethereum market [36][39] [40], which has an impact on the increase in the price of Ethereum during the period of the year.
When compared year to year (November 2020 to November 2021), the price of Ethereum in 2020 was only approximately 8.8 million IDR, while the price of Bitcoin was over 240 million IDR.
It implies that the Ethereum price has increased by 675 percent and the Bitcoin price has increased by 303.5 percent during the previous year. Ethereum is always being improved, particularly this year. Following the London Hard Fork update some time ago, Ethereum 2.0 is now accessible [34].
With the Ethereum 2.0 update, Ethereum becomes a proof of stake cryptocurrency with an auto burn feature that eliminates the amount of Ethereum that exists in order to limit the supply of Ethereum and expand the network.
Based on these results, this study found a tendency to calculate technical predictions on the Ethereum market with much more accuracy than the fundamental analysis applicable to the financial market by taking into account the historical elements of market prices, and macroeconomic conditions [30][31] considering that there are two types of crypto price patterns in general.
Therefore, there is a need for deeper research on Ethereum market predictions by paying attention to various macro aspects such as crypto market characteristics, GDP, inflation rates, exchange rates and several other macroeconomic aspects.
IV. Conclusion
The price of Ethereum over the last two years indicated that the highest price was on November 09, 2021, when the price was 4808.34 US$. It can be seen that the price of Ethereum was high at the end of 2021, but it also experienced a decline, as seen by the price chart moving downward in 2022.
The price of Ethereum was quite high towards the end of 2021 attributed to the ICO enthusiasm, which is the concept of raising funds in cryptocurrencies, but it weakened in early 2022 and has been generally bearish since then.
Meanwhile, LSTM has been successfully used in Ethereum price predictions using blockchain data, with training data of 80 percent (583 data), testing data of 20 percent (146 data), and predictions with the minimum error value produced using 50 neurons and 500 epochs. Ethereum price predictions during the first seven days of predictive data, from December 1, 2021 to December 7, 2021, continue to drop, with an error value of 1.69 percent, indicating that the prediction results are quite accurate with the real value.
Our initial efforts demonstrate the power of LSTMs in sequence learning for Ethereum market predictions, mechanical but far more unpredictable. This inspires us more continuous and interesting work, for example by including MACD and other features in the study of the feature set and evaluating its contribution, analyzing crypto types by type, time window by time window due to the high volatility of market indexes. We also plan to test more data as a learning tool by including open and closing prices, market indices, as well as financial news and even social network mood as measurement parameters.
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