Construct a graph of the value of the Russian ruble against the Egyptian pound based on data at the end of trading. Select the best ARIMA model, predict further exchange rate values ​​based on it, display it on a graph, and draw conclusions.

Here is a task that I need to do, but when I try to output the final values, I encounter NaN. I don’t know how to solve it, here is the code:

import matplotlib.pyplot as plt
import pandas as pd
import csv
import statsmodels.api as sm
from statsmodels.tsa.stattools import adfuller

def tundra():
    print('1 - Output')
    print('2 - Downloading)
    oi = input('Choose option: ')
    if oi == '1':
        plt.show()
    elif oi == '2':
        po = input('Enter a name for the saved file: ')
        plt.savefig(f'{po}.jpg')
        print(f'Graph saved as {po}.jpg')
    else:
        print('Let's do it again')
        tundra()

dates = []
rub_to_nok = []

with open('D:\Teleegram dw\mfdexport_1month_01012014_30082024.csv', 'r') as csvfile:
    plots = csv.reader(csvfile, delimiter=';')
    next(plots)
    for row in plots:
        if row[0] == 'NOK':
            dates.append(pd.to_datetime(int(row[2]), format='%Y%m%d'))
            rub_to_nok.append(float(row[6]))

data = pd.DataFrame({'Date': dates, 'RUB_to_NOK': rub_to_nok})
data.set_index('Date', inplace=True)

print("Checking for NaN in data:")
print(data.isnull().sum())
data.dropna(inplace=True)

print(data.info())
print(data.head())

result = adfuller(data['RUB_to_NOK'])
print('Dickey-Fuller test:')
print('Statistics: %f' % result[0])
print('p-value: %f' % result[1])
print('Critical values:')
for key, value in result[4].items():
    print(f'    {key}: {value}')

model = sm.tsa.ARIMA(data['RUB_to_NOK'], order=(3, 1, 0))
model_fit = model.fit()

print(model_fit.summary())

forecast = model_fit.forecast(steps=30)
forecast_index = pd.date_range(start=data.index[-1] + pd.Timedelta(days=1), periods=30, freq='D')

forecast_df = pd.DataFrame(forecast, index=forecast_index, columns=['Forecast'])

print("Predicted values:")
for index, row in forecast_df.iterrows():
    print(f"Date: {index.strftime('%Y-%m-%d')}, Prognosis: {row['Forecast']:.4f}")

plt.figure(figsize=(25, 12), facecolor='grey')
plt.gca().set_facecolor('black')
plt.plot(data.index, data['RUB_to_NOK'], label='Cost RUB/NOK', color='white', alpha=0.5, linewidth=2)
plt.plot(forecast_df.index, forecast_df['Forecast'], label='Prediction (AR(3))', color='blue', linewidth=2, linestyle='--')
plt.title('RUB to NOK value with prediction (AR(3))')
plt.xlabel('Date')
plt.ylabel('Cost in CZK')
plt.legend()
plt.grid()

tundra()

Here is the csv file:

https://drive.google.com/file/d/1W-81mTJUqLHB73_2zLV5bPG-3d3oaO4T/view?usp=sharing

I tried to check the data when checking, but it is normal. I don’t know how to draw an adequate conclusion.