from statsmodels.tsa.statespace.sarimax import SARIMAX
import pandas as pd
import itertools
import warnings
import numpy as np

import statsmodels.api as sm

try:
print(“Starting script…”)

# Load your dataset
print("Loading dataset...")
df = pd.read_csv('C:/Users/iameh/Downloads/archive/powerconsumption.csv')

# Convert 'Datetime' to datetime and set it as the index
print("Preprocessing data...")
df['Datetime'] = pd.to_datetime(df['Datetime'])
df.set_index('Datetime', inplace=True)

# Define the p, d, q parameters to take any value between 0 and 1
p = d = q = range(0, 2)

# Generate all different combinations of p, q and q triplets
pdq = list(itertools.product(p, d, q))

# Generate all different combinations of seasonal p, q and q triplets
seasonal_pdq = [(x[0], x[1], x[2], 12) for x in list(itertools.product(p, d, q))]

# Specify to ignore warning messages
warnings.filterwarnings("ignore")

# List of parameters to forecast
parameters = ['Temperature', 'Humidity', 'WindSpeed', 'GeneralDiffuseFlows', 'DiffuseFlows', 'PowerConsumption_Zone1', 'PowerConsumption_Zone2', 'PowerConsumption_Zone3']

print("Starting forecasting for each parameter...")
for parameter in parameters:
    print(f"Forecasting for {parameter}...")
    
    # Select the relevant column for forecasting
    endog = df[parameter]

    # Find the best parameters for the model
    best_aic = np.inf
    best_pdq = None
    best_seasonal_pdq = None
    temp_model = None

    print("Finding best model parameters...")
    for param in pdq:
        for param_seasonal in seasonal_pdq:

            try:
                temp_model = SARIMAX(endog,
                                     order = param,
                                     seasonal_order = param_seasonal,
                                     enforce_stationarity=False,
                                     enforce_invertibility=False)
                results = temp_model.fit()

                # print("SARIMAX{}x{}12 - AIC:{}".format(param, param_seasonal, results.aic))
                if results.aic < best_aic:
                    best_aic = results.aic
                    best_pdq = param
                    best_seasonal_pdq = param_seasonal
            except:
                continue

    print("Best SARIMAX{}x{}12 model for {} - AIC:{}".format(best_pdq, best_seasonal_pdq, parameter, best_aic))

    # Fit the best model
    print("Fitting the best model...")
    model = SARIMAX(endog,
                    order=best_pdq,
                    seasonal_order=best_seasonal_pdq,
                    enforce_stationarity=False,
                    enforce_invertibility=False)

    results = model.fit()

    # Get forecast steps ahead in future
    print("Making forecasts...")
    pred_uc = results.get_forecast(steps=52416)

    # Write the DataFrame to a CSV file
    print("Saving forecasts to CSV file...")
    pred_uc.predicted_mean.to_csv(f'C:/Users/iameh/Downloads/archive/{parameter}_forecast.csv')

    print(f"Forecasting for {parameter} completed!")

print("Script completed!")

except Exception as e:
print(“An error occurred during the execution of the script:”)
print(e)

I am using this code to generate the next years prediction. But it is crashing/shell is restarting