# -*- coding: utf-8 -*-
"""
Created on Thu Feb 27 10:37:59 2025

@author: Abdellah El Fallahi
"""

import pandas as pd
import matplotlib.pyplot as plt

filepath=r"D:/Enseignement/cours/AutomaticLearning/TP_2025/Exo1.xlsx"
data=pd.read_excel(filepath,sheet_name="ARMA(2,2)",usecols=["Value"])
data
# check stationary
from statsmodels.tsa.stattools import adfuller
stat=adfuller(data)
stat
# plot acf and pacf
from statsmodels.graphics.tsaplots import plot_acf, plot_pacf
plot_acf(data,title="Autocorrelation of data")
plot_pacf(data)
plt.show()
from statsmodels.tsa.arima.model import ARIMA
# fiting the model model MA(1)
model_MA1=ARIMA(data, order=(0,0,1))
resultMA1=model_MA1.fit()
print(resultMA1.summary())

# Print log-likelihood and AIC/BIC  of MA(1)
print("Log-Likelihood:", resultMA1.llf)
print("AIC:", resultMA1.aic)
print("BIC:", resultMA1.bic)

# fiting the model AR(2)
model_AR2=ARIMA(data, order=(2,0,0))
resultAR2=model_AR2.fit()
print(resultAR2.summary())
# Print log-likelihood and AIC/BIC of AR(2)
print("Log-Likelihood:", resultAR2.llf)
print("AIC:", resultAR2.aic)
print("BIC:", resultAR2.bic)

# another way to display aic and bic values
print(f"AIC: {resultAR2.aic}")
print(f"BIC: {resultAR2.bic}")

# Ft arma(2,1) mpdel 
model_ARMA=ARIMA(data, order=(2,0,1))
resultARMA=model_ARMA.fit()
print(resultARMA.summary())
# Fit arima model with parameters (1,1,1)
model_ARiMA=ARIMA(data, order=(2,1,1))
resultARMA=model_ARiMA.fit()
print(resultARMA.summary())
# Print log-likelihood and AIC/BIC of AR(2)
print("Log-Likelihood:", resultARMA.llf)
print("AIC:", resultARMA.aic)
print("BIC:", resultARMA.bic)

# write the AIC and BIC as a table
from tabulate import tabulate

data = [
    ["MA(1)", resultMA1.aic, resultMA1.bic],
    ["RA(2)", resultAR2.aic, resultAR2.bic],
    ["ARMA(2,1)", resultARMA.aic, resultARMA.bic]
]

headers = ["Model", "AIC", "BIC"]

print(tabulate(data, headers=headers, tablefmt="grid"))