Update app.py

app.py

@@ -2,29 +2,31 @@ import streamlit as st
 import pandas as pd
 from io import BytesIO
 from itertools import product
-from statsmodels.tsa.holtwinters import ExponentialSmoothing
+from statsmodels.tsa.statespace.sarimax import SARIMAX
 import plotly.express as px
+
 st.set_page_config(layout="wide")
-# Function to run the Exponential Smoothing Model
-def run_exp_smoothing(city_data, trend, damped_trend, seasonal, seasonal_period):
+
+# Function to run the SARIMAX Model
+def run_sarimax(city_data, order, seasonal_order):
     try:
-        model = ExponentialSmoothing(city_data, trend=trend, damped_trend=damped_trend, seasonal=seasonal, seasonal_periods=seasonal_period)
-        model_fit = model.fit(optimized=True)
+        model = SARIMAX(city_data, order=order, seasonal_order=seasonal_order, enforce_stationarity=False, enforce_invertibility=False)
+        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 model fitting: {e}")
         return None, None
 
 def create_data():
+    # Assuming you have a CSV file named 'accident_count.csv' with 'City' and 'Accident Count' columns
     data = pd.read_csv('accident_count.csv', parse_dates=True, index_col=0)
     data.index = pd.to_datetime(data.index, format='%Y%m')
     data = data.groupby('City').resample('M').sum().reset_index()
     data.index = data['Accident Month Bracket']
-    data = data.drop(['Accident Month Bracket'],axis=1)
+    data = data.drop(['Accident Month Bracket'], axis=1)
     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')
@@ -35,9 +37,9 @@ def to_excel(df):
 
 # 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 = {'order': (1, 1, 1), 'seasonal_order': (1, 1, 1, 12)}
 
-st.title("Exponential Smoothing Forecasting")
+st.title("SARIMAX Forecasting")
 
 # Data preparation
 data = create_data()
@@ -48,18 +50,21 @@ tabs = st.tabs([city for city in unique_cities])
 
 for tab, city in zip(tabs, unique_cities):
     with tab:
-        # 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'],key=city+'1')
-        damped_trend = False
-        seasonal = st.select_slider('Select Seasonal', options=['add', 'mul', None], value=st.session_state.best_params['seasonal'],key=city+'2')
-        seasonal_period = st.slider('Seasonal Period', 1, 24, value=st.session_state.best_params['seasonal_period'],key=city+'3')
+        # SARIMAX specific sliders
+        p = st.slider('AR Order (p)', 0, 5, value=st.session_state.best_params['order'][0], key=city+'p')
+        d = st.slider('Differencing Order (d)', 0, 2, value=st.session_state.best_params['order'][1], key=city+'d')
+        q = st.slider('MA Order (q)', 0, 5, value=st.session_state.best_params['order'][2], key=city+'q')
+        P = st.slider('Seasonal AR Order (P)', 0, 5, value=st.session_state.best_params['seasonal_order'][0], key=city+'P')
+        D = st.slider('Seasonal Differencing Order (D)', 0, 2, value=st.session_state.best_params['seasonal_order'][1], key=city+'D')
+        Q = st.slider('Seasonal MA Order (Q)', 0, 5, value=st.session_state.best_params['seasonal_order'][2], key=city+'Q')
+        S = st.slider('Seasonal Period (S)', 1, 24, value=st.session_state.best_params['seasonal_order'][3], key=city+'S')
 
         city_data = data[data['City'] == city]['Accident Count']
-        forecast, aic = run_exp_smoothing(city_data, trend, damped_trend, seasonal, seasonal_period)
+        forecast, aic = run_sarimax(city_data, (p, d, q), (P, D, Q, S))
 
         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"Non-Seasonal Order: {(p, d, q)}, Seasonal Order: {(P, D, Q, S)}")
             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_df = pd.DataFrame(forecast, columns=['Forecast'])
@@ -72,24 +77,24 @@ for tab, city in zip(tabs, unique_cities):
         if st.button(f'Run Grid Search for {city}'):
             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
+            for param_set in product(range(3), repeat=3):  # Adjust the range and repeat parameters as needed
+                for seasonal_param_set in product(range(3), repeat=4):  # Adjust for seasonal parameters
+                    _, temp_aic = run_sarimax(city_data, param_set, seasonal_param_set+(12,))
+                    if temp_aic and temp_aic < best_aic:
+                        best_aic = temp_aic
+                        best_params = (param_set, seasonal_param_set+(12,))
 
             # 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]
+                'order': best_params[0],
+                'seasonal_order': best_params[1]
             }
             st.write(f"Best Parameters for {city}: {best_params} with AIC: {best_aic}")
 
-        
         # Export to Excel button
         if st.button(f'Export {city} 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=f'{city}_forecast.xlsx', mime='application/vnd.ms-excel')
+            st.download_button(label='📥 Download Excel', data=excel_data, file_name=f'{city}_forecast.xlsx', mime='application/vnd.ms-excel')
+
+# Rest of your code