diff --git a/Code for Method II_WithTimesSeries b/Code for Method II_WithTimesSeries
deleted file mode 100644
index d056004bebbf517d5049e5d7cdf51349631cdc95..0000000000000000000000000000000000000000
--- a/Code for Method II_WithTimesSeries	
+++ /dev/null
@@ -1,90 +0,0 @@
-import xarray as xr
-import numpy as np
-import matplotlib.pyplot as plt
-from statsmodels.tsa.arima.model import ARIMA
-from sklearn.metrics import mean_squared_error
-
-def plot_arima_forecast(order, years, time_series, future_years, label, color, original_legend_added=False):
-    model = ARIMA(time_series, order=order)
-    model_fit = model.fit()
-    forecast = model_fit.forecast(steps=len(future_years))
-    
-    # Add legend for original data only once
-    if not original_legend_added:
-        plt.plot(years, time_series, label='Original Data', marker='o', color='green')
-        original_legend_added = True
-
-    plt.axvline(x=2022, color='gray', linestyle='--')  # Vertical line at the year 2022
-    plt.plot(future_years, forecast, label=label, linestyle='dashed', marker='o', color=color)
-
-    # Hindcasts for lead times 1, 2, 3, 4, and 5 years
-    for lead_time in [1, 2, 3, 4, 5]:
-        hindcast_years = range(1981 + lead_time, 2023)
-        hindcast = model_fit.predict(start=lead_time, end=len(years) - 1)
-        plt.plot(hindcast_years, hindcast, linestyle='dashed', marker='o', color=color)
-
-    # Calculate RMSE for each hindcast
-    rmse_values = []
-    for lead_time in [1, 2, 3, 4, 5]:
-        hindcast = model_fit.predict(start=lead_time, end=len(years) - 1)
-        rmse = np.sqrt(mean_squared_error(time_series[lead_time:], hindcast))
-        rmse_values.append(rmse)
-
-    return rmse_values, original_legend_added
-
-# Specify the range of years
-years = range(1981, 2023)
-
-# Initialize a list to store spatial averages
-spatial_averages = []
-
-# Loop through each year
-for year in years:
-    # Load the count file for the current year
-    count_data = xr.open_dataset(f'/home/u/u301871/counts/count_{year}.nc')
-    cluster_count = count_data['cluster_count'].values  # Adjust variable name
-
-    # Calculate the spatial average
-    spatial_average = np.mean(cluster_count)
-
-    # Append the result to the list
-    spatial_averages.append(spatial_average)
-
-    # Close the count dataset to free up resources
-    count_data.close()
-
-# Create a time series
-time_series = np.floor(spatial_averages)
-
-# Plot ARIMA forecasts with different parameters
-future_years = range(2023, 2034)
-original_legend_added = False
-rmse_1, original_legend_added = plot_arima_forecast(order=(20, 1, 3), years=years, time_series=time_series, future_years=future_years, label='ARIMA Forecast (20, 1, 3)', color='blue', original_legend_added=original_legend_added)
-rmse_2, original_legend_added = plot_arima_forecast(order=(20, 1, 1), years=years, time_series=time_series, future_years=future_years, label='ARIMA Forecast (20, 1, 1)', color='black', original_legend_added=original_legend_added)
-rmse_3, original_legend_added = plot_arima_forecast(order=(20, 1, 5), years=years, time_series=time_series, future_years=future_years, label='ARIMA Forecast (20, 1, 5)', color='red', original_legend_added=original_legend_added)
-rmse_4, original_legend_added = plot_arima_forecast(order=(20, 1, 2), years=years, time_series=time_series, future_years=future_years, label='ARIMA Forecast (20, 1, 2)', color='purple', original_legend_added=original_legend_added)
-rmse_5, original_legend_added = plot_arima_forecast(order=(20, 1, 4), years=years, time_series=time_series, future_years=future_years, label='ARIMA Forecast (20, 1, 4)', color='yellow', original_legend_added=original_legend_added)
-
-# Add legend to the first plot
-plt.legend()
-
-# Save the first plot as timeseries.png
-plt.savefig('timeseries.png')
-
-# Plot RMSE values
-lead_times = [1, 2, 3, 4, 5]
-plt.figure()
-for i, rmse_values in enumerate([rmse_1, rmse_2, rmse_3, rmse_4, rmse_5]):
-    plt.plot(lead_times, rmse_values, label=f'ARIMA Forecast (20, 1, {i + 1})', marker='o')
-
-plt.xlabel('Lead Time (Years)')
-plt.ylabel('RMSE')
-plt.title('RMSE of ARIMA Forecasts for Different Lead Times')
-plt.legend()
-plt.grid(True)
-
-# Save the second plot as rmse.png
-plt.savefig('rmse.png')
-
-# Show the plots
-plt.show()