Update app.py

app.py

@@ -1,8 +1,9 @@
-import streamlit as st
 import pandas as pd
 from io import BytesIO
-from itertools import product
+import streamlit as st
 from statsmodels.tsa.holtwinters import ExponentialSmoothing
+from statsmodels.tsa.statespace.sarimax import SARIMAX
+from itertools import product
 import plotly.express as px
 
 # Function to run the Exponential Smoothing Model
@@ -15,6 +16,16 @@ def run_exp_smoothing(city_data, trend, damped_trend, seasonal, seasonal_period)
         st.error(f"An error occurred during model fitting: {e}")
         return None, None
 
+# Function to run SARIMAX Model
+def run_sarimax(city_data, order, seasonal_order):
+    try:
+        model = SARIMAX(city_data, order=order, seasonal_order=seasonal_order)
+        model_fit = model.fit(disp=False)
+        return model_fit.forecast(steps=6), model_fit.aic
+    except Exception as e:
+        st.error(f"An error occurred during SARIMAX model fitting: {e}")
+        return None, None
+
 def create_data():
     data = pd.read_csv('accident_count.csv', parse_dates=True, index_col=0)
     data.index = pd.to_datetime(data.index, format='%Y%m')
@@ -24,7 +35,6 @@ def create_data():
     data.index = data.index.strftime('%Y-%m')
     return data
 
-# Function to convert DataFrame to Excel
 def to_excel(df):
     output = BytesIO()
     writer = pd.ExcelWriter(output, engine='xlsxwriter')
@@ -33,61 +43,74 @@ def to_excel(df):
     processed_data = output.getvalue()
     return processed_data
 
-# Initialize session state for best parameters
 if 'best_params' not in st.session_state:
-    st.session_state.best_params = {'trend': None, 'damped_trend': False, 'seasonal': None, 'seasonal_period': 12}
+    st.session_state.best_params = {'trend': None, 'damped_trend': False, 'seasonal': None, 'seasonal_period': 12, 'model_type': 'ExpSmoothing'}
 
-st.title("Exponential Smoothing Forecasting")
+st.title("Exponential Smoothing and SARIMAX Forecasting")
 
-# Data preparation
 data = create_data()
 unique_cities = data['City'].unique()
 
-# Select a city
 selected_city = st.selectbox('Select a City', unique_cities)
 
-# Sliders for parameter adjustment, using session state values as defaults
-trend = st.select_slider('Select Trend', options=['add', 'mul', None], value=st.session_state.best_params['trend'])
-damped_trend = False#st.checkbox('Damped Trend', value=st.session_state.best_params['damped_trend'])
-seasonal = st.select_slider('Select Seasonal', options=['add', 'mul', None], value=st.session_state.best_params['seasonal'])
-seasonal_period = st.slider('Seasonal Period', 1, 24, value=st.session_state.best_params['seasonal_period'])
+model_type = st.selectbox('Select Model Type', ['ExpSmoothing', 'SARIMAX'])
+
+if model_type == 'ExpSmoothing':
+    trend = st.select_slider('Select Trend', options=['add', 'mul', None], value=st.session_state.best_params['trend'])
+    damped_trend = st.checkbox('Damped Trend', value=st.session_state.best_params['damped_trend'])
+    seasonal = st.select_slider('Select Seasonal', options=['add', 'mul', None], value=st.session_state.best_params['seasonal'])
+    seasonal_period = st.slider('Seasonal Period', 1, 24, value=st.session_state.best_params['seasonal_period'])
+elif model_type == 'SARIMAX':
+    p = st.slider('AR Order (p)', 0, 5, 0)
+    d = st.slider('Differencing (d)', 0, 2, 1)
+    q = st.slider('MA Order (q)', 0, 5, 0)
+    P = st.slider('Seasonal AR Order (P)', 0, 2, 0)
+    D = st.slider('Seasonal Differencing (D)', 0, 2, 1)
+    Q = st.slider('Seasonal MA Order (Q)', 0, 2, 0)
+    S = st.slider('Seasonal Period (S)', 1, 24, 12)
 
 city_data = data[data['City'] == selected_city]['Accident Count']
-forecast, aic = run_exp_smoothing(city_data, trend, damped_trend, seasonal, seasonal_period)
+
+if model_type == 'ExpSmoothing':
+    forecast, aic = run_exp_smoothing(city_data, trend, damped_trend, seasonal, seasonal_period)
+elif model_type == 'SARIMAX':
+    order = (p, d, q)
+    seasonal_order = (P, D, Q, S)
+    forecast, aic = run_sarimax(city_data, order, seasonal_order)
 
 if forecast is not None:
     st.write(f"Best Parameters with AIC: {aic}")
-    st.write(f"Trend: {trend}, Damped Trend: {damped_trend}, Seasonal: {seasonal}, Seasonal Period: {seasonal_period}")
+    st.write(f"Forecast:")
     forecast_index = pd.date_range(start=city_data.index[-1], periods=7, freq='M')[1:]
-    forecast_index = forecast_index.to_period('M')  # Convert to period index with monthly frequency
+    forecast_index = forecast_index.to_period('M')
     forecast_df = pd.DataFrame(forecast, columns=['Forecast'])
     forecast_df = forecast_df.round(0)
     st.table(forecast_df)
     fig = px.line(forecast_df, x=forecast_df.index, y="Forecast")
     st.plotly_chart(fig)
 
-# Grid search button
+# Grid Search Logic for Both Models
 if st.button('Run Grid Search'):
     best_aic = float('inf')
     best_params = None
-    for param_set in product(['add', 'mul', None], [False], ['add', 'mul', None], [12]):
-        _, temp_aic = run_exp_smoothing(city_data, *param_set)
-        if temp_aic and temp_aic < best_aic:
-            best_aic = temp_aic
-            best_params = param_set
-
-    # Updating session state with the best parameters
-    st.session_state.best_params = {
-        'trend': best_params[0],
-        'damped_trend': best_params[1],
-        'seasonal': best_params[2],
-        'seasonal_period': best_params[3]
-    }
+
+    if model_type == 'ExpSmoothing':
+        for param_set in product(['add', 'mul', None], [False], ['add', 'mul', None], [12]):
+            _, temp_aic = run_exp_smoothing(city_data, *param_set)
+            if temp_aic and temp_aic < best_aic:
+                best_aic = temp_aic
+                best_params = param_set
+    elif model_type == 'SARIMAX':
+        for param_set in product(range(3), range(2), range(3), range(2), range(2), range(2), [12]):
+            _, temp_aic = run_sarimax(city_data, param_set[:3], param_set[3:])
+            if temp_aic and temp_aic < best_aic:
+                best_aic = temp_aic
+                best_params = param_set
+
+    st.session_state.best_params = best_params
     st.write(f"Best Parameters: {best_params} with AIC: {best_aic}")
 
-    
-# Export to Excel button
 if st.button('Export to Excel'):
     df_to_export = forecast_df
     excel_data = to_excel(df_to_export)
-    st.download_button(label='📥 Download Excel', data=excel_data, file_name='forecast.xlsx', mime='application/vnd.ms-excel')
+    st.download_button(label='📥 Download Excel', data=excel_data, file_name='forecast.xlsx', mime='application/vnd.ms-excel')