DataScienceGuild/ARIMA_test

1# Import necessary libraries2import streamlit as st3import pandas as pd4from pmdarima import auto_arima5import matplotlib.pyplot as plt6 7# Title of the Streamlit app8st.title('Auto ARIMA Time Series Analysis')9 10# Upload CSV data11uploaded_file = st.file_uploader("Choose a CSV file", type='csv')12 13if uploaded_file is not None:14    # Read the uploaded CSV file with pandas15    df = pd.read_csv(uploaded_file)16 17    # Convert timestamp column to datetime format and set it as index 18    df['timestamp'] = pd.to_datetime(df['timestamp'])19    df.set_index('timestamp', inplace=True)20 21    # Perform Auto ARIMA analysis on value column22    model = auto_arima(df['value'], trace=True, error_action='ignore', suppress_warnings=True)23    24    # Fit the model and get predictions for next 10 periods 25    model.fit(df['value'])26    predictions = model.predict(n_periods=10)27    28    # Display model summary in Streamlit app 29    st.write(model.summary())30 31    # Create a plot with Matplotlib and display it in Streamlit app 32    fig, ax = plt.subplots()33    34    ax.plot(df.index, df['value'], label='Original')35    36    prediction_index = pd.date_range(start=df.index[-1], periods=11)[1:]37    38    ax.plot(prediction_index, predictions, label='Predicted')39    40    plt.title('Value vs Timestamp')41    42    plt.legend()43    44    st.pyplot(fig)45 46    # Create a plot with Matplotlib and display it in Streamlit app 47    fig2, ax2 = plt.subplots()48    49    ax2.plot(df.index, df['value'], label='Original')50    51    prediction_index = pd.date_range(start=df.index[-1], periods=11)[1:]52    53    # ax2.plot(prediction_index, predictions, label='Predicted')54    55    plt.title('Value vs Timestamp original only')56    57    plt.legend()58    59    st.pyplot(fig2)