Create app.py

app.py

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+# Import necessary libraries
+import streamlit as st
+import pandas as pd
+from pmdarima import auto_arima
+import matplotlib.pyplot as plt
+
+# Title of the Streamlit app
+st.title('Auto ARIMA Time Series Analysis')
+
+# Upload CSV data
+uploaded_file = st.file_uploader("Choose a CSV file", type='csv')
+
+if uploaded_file is not None:
+    # Read the uploaded CSV file with pandas
+    df = pd.read_csv(uploaded_file)
+
+    # Convert timestamp column to datetime format and set it as index 
+    df['timestamp'] = pd.to_datetime(df['timestamp'])
+    df.set_index('timestamp', inplace=True)
+
+    # Perform Auto ARIMA analysis on value column
+    model = auto_arima(df['value'], trace=True, error_action='ignore', suppress_warnings=True)
+    
+    # Fit the model and get predictions for next 10 periods 
+    model.fit(df['value'])
+    predictions = model.predict(n_periods=10)
+    
+    # Display model summary in Streamlit app 
+    st.write(model.summary())
+
+    # Create a plot with Matplotlib and display it in Streamlit app 
+    fig, ax = plt.subplots()
+    
+    ax.plot(df.index, df['value'], label='Original')
+    
+    prediction_index = pd.date_range(start=df.index[-1], periods=11)[1:]
+    
+    ax.plot(prediction_index, predictions, label='Predicted')
+    
+    plt.title('Value vs Timestamp')
+    
+    plt.legend()
+    
+    st.pyplot(fig)