USING ARIMA(Auto regressive integrated moving average) model to predict the stock price

ARIMA is a popular time series model used for forecasting. In this article we will use arima model to predict the stock prices of microsoft.

The stock price data is downloaded from Yahoo Finance website. You can open the website, search for microsoft stocks. Go to history and there you will find the download option to download the stock prices.  

I used daily stock data of microsoft from 01-01-2010 to 11-06-2021.

PS: To remove warnings I used :

from warnings import simplefilter

simplefilter(action='ignore', category = FutureWarning)

Also use 'from datatime import datetime' instead of 'from pandas import datetime'

from warnings import simplefilter

simplefilter(action='ignore', category = FutureWarning)

Also use 'from datatime import datetime' instead of 'from pandas import datetime'

So here's the code:

 

import numpy as np 

import pandas as pd 

import matplotlib.pyplot as plt

from pandas.plotting import lag_plot

from datetime import datetime

from statsmodels.tsa.arima_model import ARIMA

from sklearn.metrics import mean_squared_error

from warnings import simplefilter

simplefilter(action='ignore', category=FutureWarning)

data = pd.read_csv('microsoft stock data.csv')

data.head()

data.shape

plt.figure()
lag_plot(data['Open'], lag=3)
plt.title('MICROSOFT Stock - Autocorrelation plot with lag = 3')
plt.show()

#For modeling, we train the model using 70 percent of data, And test the model using 30 percent of data

train_data, test_data = data[0:int(len(data)*0.7)], data[int(len(data)*0.7):]

training_data = train_data['Close'].values

test_data = test_data['Close'].values

history = [x for x in training_data]

model_predictions = []

N_test_observations = len(test_data)

for time_point in range(N_test_observations):

model = ARIMA(history, order=(4,1,0))

model_fit = model.fit(disp=0)

output = model_fit.forecast()

yhat = output[0]

model_predictions.append(yhat)

true_test_value = test_data[time_point]

history.append(true_test_value)

MSE_error = mean_squared_error(test_data, model_predictions)

print('Testing Mean Squared Error is {}'.format(MSE_error))

test_set_range = data[int(len(data)*0.7):].index

plt.plot(test_set_range, model_predictions, color='blue', marker='o', linestyle='solid',label='Predicted Price')

plt.plot(test_set_range, test_data, color='red', label='Actual Price')

plt.title('Microsoft Prices Prediction')

plt.xlabel('Date')

plt.ylabel('Prices')

plt.xticks(np.arange(2000,2990,400), df.Date[2000:2990:400])

plt.legend()

plt.show()

Conclusion: The error is less and thus ARIMA seems to be a good predictive model.