Upload 3 files

AirPassengers.csv

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+Month,#Passengers
+1949-01,112
+1949-02,118
+1949-03,132
+1949-04,129
+1949-05,121
+1949-06,135
+1949-07,148
+1949-08,148
+1949-09,136
+1949-10,119
+1949-11,104
+1949-12,118
+1950-01,115
+1950-02,126
+1950-03,141
+1950-04,135
+1950-05,125
+1950-06,149
+1950-07,170
+1950-08,170
+1950-09,158
+1950-10,133
+1950-11,114
+1950-12,140
+1951-01,145
+1951-02,150
+1951-03,178
+1951-04,163
+1951-05,172
+1951-06,178
+1951-07,199
+1951-08,199
+1951-09,184
+1951-10,162
+1951-11,146
+1951-12,166
+1952-01,171
+1952-02,180
+1952-03,193
+1952-04,181
+1952-05,183
+1952-06,218
+1952-07,230
+1952-08,242
+1952-09,209
+1952-10,191
+1952-11,172
+1952-12,194
+1953-01,196
+1953-02,196
+1953-03,236
+1953-04,235
+1953-05,229
+1953-06,243
+1953-07,264
+1953-08,272
+1953-09,237
+1953-10,211
+1953-11,180
+1953-12,201
+1954-01,204
+1954-02,188
+1954-03,235
+1954-04,227
+1954-05,234
+1954-06,264
+1954-07,302
+1954-08,293
+1954-09,259
+1954-10,229
+1954-11,203
+1954-12,229
+1955-01,242
+1955-02,233
+1955-03,267
+1955-04,269
+1955-05,270
+1955-06,315
+1955-07,364
+1955-08,347
+1955-09,312
+1955-10,274
+1955-11,237
+1955-12,278
+1956-01,284
+1956-02,277
+1956-03,317
+1956-04,313
+1956-05,318
+1956-06,374
+1956-07,413
+1956-08,405
+1956-09,355
+1956-10,306
+1956-11,271
+1956-12,306
+1957-01,315
+1957-02,301
+1957-03,356
+1957-04,348
+1957-05,355
+1957-06,422
+1957-07,465
+1957-08,467
+1957-09,404
+1957-10,347
+1957-11,305
+1957-12,336
+1958-01,340
+1958-02,318
+1958-03,362
+1958-04,348
+1958-05,363
+1958-06,435
+1958-07,491
+1958-08,505
+1958-09,404
+1958-10,359
+1958-11,310
+1958-12,337
+1959-01,360
+1959-02,342
+1959-03,406
+1959-04,396
+1959-05,420
+1959-06,472
+1959-07,548
+1959-08,559
+1959-09,463
+1959-10,407
+1959-11,362
+1959-12,405
+1960-01,417
+1960-02,391
+1960-03,419
+1960-04,461
+1960-05,472
+1960-06,535
+1960-07,622
+1960-08,606
+1960-09,508
+1960-10,461
+1960-11,390
+1960-12,432

app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+from statsmodels.tsa.statespace.sarimax import SARIMAX
+from sklearn.metrics import mean_absolute_percentage_error
+
+# Streamlit app layout
+st.set_page_config(layout="wide")
+st.title("Air Passenger Data Analysis and Forecasting")
+st.write("Time Series Analysis and Forecasting with SARIMAX")
+
+# Load data
+data_path = "AirPassengers.csv"  # Ensure this file is in the correct directory
+data = pd.read_csv(data_path, parse_dates=['Month'], index_col='Month')
+data.index.freq = 'MS'  # Set the frequency to Month Start to avoid warnings
+st.write("Data Preview", data.head())
+
+# Sidebar for SARIMAX parameters
+st.sidebar.header("SARIMAX Model Parameters")
+p = st.sidebar.slider("AR order (p)", 0, 5, 1)
+d = st.sidebar.slider("Difference order (d)", 0, 2, 1)
+q = st.sidebar.slider("MA order (q)", 0, 5, 1)
+P = st.sidebar.slider("Seasonal AR order (P)", 0, 5, 1)
+D = st.sidebar.slider("Seasonal Difference order (D)", 0, 2, 1)
+Q = st.sidebar.slider("Seasonal MA order (Q)", 0, 5, 1)
+s = st.sidebar.selectbox("Seasonal period (s)", [12])  # Monthly seasonality
+forecast_steps = st.sidebar.slider("Forecast Steps (Months)", min_value=1, max_value=36, value=12)
+
+# Display data preview and time series plot
+st.subheader("Air Passenger Traffic Over Time")
+fig, ax = plt.subplots()
+ax.plot(data.index, data['#Passengers'], color='blue', label="Observed")
+ax.set_xlabel("Date")
+ax.set_ylabel("Number of Passengers")
+ax.set_title("Monthly Air Passenger Data")
+ax.legend()
+st.pyplot(fig)
+
+# Train the model and forecast
+if st.button("Train Model and Forecast"):
+    try:
+        # Split data into training and test sets
+        train_data = data[:-forecast_steps]  # Use all data except the last 'forecast_steps' for training
+        test_data = data[-forecast_steps:]   # Use the last 'forecast_steps' data points for testing
+
+        # Fit the SARIMAX model on the training data
+        model = SARIMAX(train_data['#Passengers'],
+                        order=(p, d, q),
+                        seasonal_order=(P, D, Q, s),
+                        enforce_stationarity=False,
+                        enforce_invertibility=False)
+        sarimax_model = model.fit(disp=False)
+        st.success("Model training completed successfully.")
+
+        # Display Model Summary
+        st.subheader("Model Summary")
+        st.text(sarimax_model.summary())
+
+        # Generate forecast for the test period to evaluate accuracy
+        forecast = sarimax_model.get_forecast(steps=forecast_steps)
+        forecast_df = forecast.summary_frame(alpha=0.05)  # 95% confidence intervals
+
+        # Calculate accuracy (MAPE)
+        forecasted_values = forecast_df['mean']
+        actual_values = test_data['#Passengers']
+        mape = mean_absolute_percentage_error(actual_values, forecasted_values) * 100  # MAPE in percentage
+
+        # Display the accuracy
+        st.subheader("Forecast Accuracy")
+        st.write(f"Mean Absolute Percentage Error (MAPE): {mape:.2f}%")
+        st.write(f"Accuracy: {100 - mape:.2f}%")
+
+        # Display forecast data
+        st.subheader("Forecast Data")
+        st.write(forecasted_values)
+
+        # Display confidence intervals
+        lower_conf_int = forecast_df['mean_ci_lower'].rename('lower #Passengers')
+        upper_conf_int = forecast_df['mean_ci_upper'].rename('upper #Passengers')
+        forecast_confidence_df = pd.concat([lower_conf_int, upper_conf_int], axis=1)
+        st.subheader("Forecast Confidence Interval")
+        st.write(forecast_confidence_df)
+
+        # Plot forecasted values with confidence intervals
+        fig, ax = plt.subplots()
+        ax.plot(data.index, data['#Passengers'], label='Observed', color='blue')
+        ax.plot(forecasted_values.index, forecasted_values, label='Forecast', color='red')
+        ax.fill_between(forecasted_values.index, lower_conf_int, upper_conf_int, color='pink', alpha=0.3)
+        ax.set_xlabel("Date")
+        ax.set_ylabel("Number of Passengers")
+        ax.set_title("Air Passenger Forecast with Accuracy")
+        ax.legend()
+        st.pyplot(fig)
+
+    except Exception as e:
+        st.error(f"An error occurred during model training or forecasting: {e}")

requirements.txt

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+streamlit
+pandas
+numpy
+matplotlib
+statsmodels
+scikit-learn