hanumanth26 / tsa_exp7 Goto Github PK

Developed by: hanumanth rao
Reg No: 212221240016

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from statsmodels.tsa.stattools import adfuller
from statsmodels.graphics.tsaplots import plot_acf, plot_pacf
from statsmodels.tsa.ar_model import AutoReg
from sklearn.metrics import mean_squared_error

data = pd.read_csv("/content/Temperature.csv")  
data['date'] = pd.to_datetime(data['date'])
data.set_index('date', inplace=True)

result = adfuller(data['temp']) 
print('ADF Statistic:', result[0])
print('p-value:', result[1])

train_data = data.iloc[:int(0.8*len(data))]
test_data = data.iloc[int(0.8*len(data)):]

lag_order = 13
model = AutoReg(train_data['temp'], lags=lag_order)
model_fit = model.fit()

plot_acf(data['temp'])
plt.title('Autocorrelation Function (ACF)')
plt.show()
plot_pacf(data['temp'])
plt.title('Partial Autocorrelation Function (PACF)')
plt.show()

predictions = model_fit.predict(start=len(train_data), end=len(train_data)+len(test_data)-1)

mse = mean_squared_error(test_data['temp'], predictions)
print('Mean Squared Error (MSE):', mse)

plt.plot(test_data.index, test_data['temp'], label='Test Data')
plt.plot(test_data.index, predictions, label='Predictions')
plt.xlabel('Date')
plt.ylabel('Temperature')
plt.title('AR Model Predictions vs Test Data')
plt.legend()
plt.show()