# Research Paper On RoninAi Machine Learning and Ai Predictive Model

Table of Contents

## Neural Network Analysis on Cryptocurrency Price Prediction

A Misnik, S Krutalevich, S Prakapenka, P Borovykh and M Vasiliev

1State Institution of Higher Professional Education “Belarusian-Russian University”, Mogilev,

Belarus

2 RoninAI Lab, New York, USA

**Abstract.** The neural networks are tool for approximation universal series of data, but their

precision highly depends on a adequate set of inputs. Cryptocurrencies experience high levels

of volatility due to absence of agreed upon pricing methodologies behind its valuation. In this

article we analyze approaches of obtaining additional inputs for neural networks and explore

their influence on its precision.

**1. Introduction**

In this study, we will estimate the price of Bitcoin (BTC), using market data and try to analyze

improvement of the neural network’s precision by adding subsequent factors such as social and time

factor.

Study presented in this paper will include two neural networks architectures: Multilayer perceptron

(MLP) and Long short-term memory (LSTM) neural networks. MLP is the most common architecture,

providing acceptable accuracy in a large number of tasks, and LSTM is considered to be more

accurate in the tasks of time series prediction

Results are tested and based on neural networks created by RoninAI Lab. RoninAI uses various

neural networks for cryptocurrency rate prediction, lending hands-on data and analysis to this study.

**2. Gathering market data**

A number of sources mention the possibility of predicting price of an asset using market data. To test

prediction accuracy of the market data, we started out by collecting historical minute-based market

data from one of the oldest and biggest cryptocurrency exchange – Kraken. Market data inputs are

listed in Table 1.

**3. Training on market data**

After training MLP and LSTM neural networks using market data as inputs and next-minute price of

BTC as output, models returned results presented on Figure 1.

The precision of predictions by both neural networks came out sufficiently low. LSTM showed

slightly better accuracy.

**4. Extending inputs with time factor**

Expert time factor is a factor representing activity level of exchanges. Retraining neural networks with

additional input of the time factor produced results shown on Figure 2.

Despite sufficiently low level of precision, predictions produced by both MLP and LSTM turned

out to be much better compared to predictions based on market data alone.

**5. Extending inputs with social factor**

It is estimated that the majority of market players in the cryptocurrency space are retail investors

making their investment and trading decisions based on the condition of visual charts. An example of

such chart is shown on Figure 3.

We wanted test the importance of visual charts on predictive power of neural networks.

Let’s consider an algorithm that allows us to identify some complex indicators of a numerical

series, based on which we can predict trader’s subjective assessment on appearance of chart.

In general, any series of data can be considered as a sum of linear and harmonic components.

The purpose of further research is to investigate the algorithm for isolating these components and

their normalization.

The proposed algorithm assumes the following stages.

**1.** Definition of the minimal element Emin of the series E.

**2.** Carrying out the subtraction operation E’= Ei-Emin.

**3.** Approximation of the series E’ by a polynomial

E1 = a (0) + a (1) * n, (1)

where a(0), a(1) are the approximation coefficients; n-discrete values of the time axis.

**4.** Calculation of the coefficient of relative change in the linear component over the period T by

the formula:

EL = a (1) * T * 100 / Emin, (2)

The value of EL is relative and does not depend on the absolute value of the series. If the quantity

EL > 0, the linear component increases.

**5.** To estimate the harmonic component of the series, we perform a Fourier transform (FT) for the

series E’.

**6.** Determine the moduli of the oscillation amplitudes in the frequency domain A(w). Carry out

filtering of frequencies according to the amplitude values.

To analyze the efficiency of the proposed algorithm, we used MATLAB environment.

Step 1. The linear component is constant. Harmonic component is absent. The results are shown on

Figure 4. The value of the coefficient EL displayed on the second chart. In this case, EL = 0.

Step 2. The linear component grows. Harmonic component is absent. The results are shown in

Figure 5. The coefficient EL = 20%. The spectrum modules have values in the low-frequency range

(1-2 Hz.). It should be remembered that the main frequency band for the module of the real sequence

lies in the interval 0 <k <= N / 2-1.

Therefore, frequencies above 15 Hz in our example should be ignored. If it is necessary to analyze

higher frequencies, we will increase N – the number of sampling points.

Step 3. The linear component decreases. Harmonic component is absent. The results are shown in

Figure 6.

We apply the proposed algorithm to the data presented in Figure 3. The result is shown in Figure 9.

The figure shows that the linear component predicts a downtrend with a rate of 4.1% per period.

Modules of amplitudes of harmonic components have influence in frequencies up to 10 Hz.

The proposed algorithm distinguishes the linear and harmonic components of the numerical series.

The proposed coefficient EL – can act as a measure of the trend of changes in the values of a

numerical series.

Moduli of the amplitude of the oscillations in the frequency domain after the Fourier

transformation can act as a measure of the estimation of the vibrational component of a series of data.

In our case we’ve extended inputs of neural networks with 4 new parameters: coefficient EL, and 3

harmonic component ranges: 0 to 10Hz, 10 to 30Hz, 30Hz and higher.

After calculating aforementioned parameters for each row of our dataset MLP and LSTM networks

have been retrained. The results of training are shown on Figure 10. The precision of predictions by both MLP and LSTM networks appeared to be much better

compared to previous results.

**6. Extending inputs with social networks factor**

Recently social networks became a powerful phenomena influencing the opinions of people on various

issues.

To make our predictions more accurate we attempted to use data obtained from social networks.

We have extended our inputs with parameters extracted from social networks on BTC-related

topics. Results of neural networks training are shown on Figure 11.

**7. Results**

As shown, the extension of market data inputs with additional relevant parameters can significantly

increase the precision of predictions.

The method, described in chapter V can be used on different datasets, to provide additional input

parameters.

In our experiments LSTM networks have shown slightly better precision, then MLP.

**8. References**

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