Best Time Series Forecasting Web App using Streamlit

Time Series Forecasting Web App using Streamlit

Introduction

Time series forecasting is a powerful technique used to predict future values based on historical data points collected over time. Whether you’re predicting stock prices, weather patterns, sales figures, or website traffic, time series analysis helps businesses and researchers make informed decisions about the future.

In this comprehensive guide, we’ll build a complete Time Series Forecasting Web App using Python and Streamlit. This interactive application allows users to upload their own datasets and apply different forecasting models like ARIMA, LSTM, and Prophet without writing a single line of code. The app provides an intuitive interface that makes complex machine learning accessible to everyone, from beginners to experienced data scientists.

Overview

Time Series Forecasting Web App is an easy and powerful tool made using Python and Streamlit. It lets users do forecasting using popular models like ARIMA, LSTM, and Prophet.

It’s made to be accurate and simple to use. Great for developers, data analysts, or businesses who want to predict future trends from time-based data.

Project Details

Technology Stack

Language/s Used: Python

Python Version (Recommended): 3.8+

Database: No external database required

Type: Web Application

Developer: UPDATEGADH

Available Features

The project includes the following comprehensive features:

• Upload time series datasets in CSV format
• Interactive visualization with Plotly charts
• Data preprocessing & validation utilities
• Multiple forecasting models:
  • ARIMA (AutoRegressive Integrated Moving Average)
  • LSTM (Long Short-Term Memory Neural Networks)
  • Prophet (Facebook’s forecasting tool)
• Model performance evaluation & comparison
• Clean, responsive UI built with Streamlit
• Export forecasted results to CSV
• Real-time model training progress indicators
• No external database needed works entirely with uploaded data

Step-by-Step Implementation Guide

Step 1: Setting Up the Environment

First, create a new directory for your project and set up a virtual environment:

mkdir time_series_app
cd time_series_app
python -m venv venv
source venv/bin/activate  # On Windows: venvScriptsactivate

Step 2: Installing Required Libraries

Create a requirements.txt file with the following dependencies:

streamlit==1.28.1
pandas==2.0.3
numpy==1.24.3
plotly==5.15.0
scikit-learn==1.3.0
statsmodels==0.14.0
prophet==1.1.4
tensorflow==2.13.0
matplotlib==3.7.2
seaborn==0.12.2

Install the dependencies:

pip install -r requirements.txt

Step 3: Complete Code Example

Create the main application file app.py:

import streamlit as st

import pandas as pd

import numpy as np

import plotly.express as px

import plotly.graph_objects as go

from prophet import Prophet

from sklearn.metrics import mean_absolute_error, mean_squared_error

import warnings

warnings.filterwarnings('ignore')






# Set page configuration

st.set_page_config(

    page_title="Time Series Forecasting App",

    page_icon="",

    layout="wide"

)
# Main title and description

st.title(" Time Series Forecasting Web App")

st.markdown("Upload your time series data and get predictions using advanced ML models!")
# Sidebar for navigation

st.sidebar.header("Navigation")

option = st.sidebar.selectbox(

    "Choose an option:",

    ["Data Upload", "Data Visualization", "Model Training", "Forecasting"]

)
# Initialize session state

if 'data' not in st.session_state:

    st.session_state.data = None

if 'trained_model' not in st.session_state:

    st.session_state.trained_model = None
# Data Upload Section

if option == "Data Upload":

    st.header(" Upload Your Time Series Data")
    uploaded_file = st.file_uploader(

        "Choose a CSV file",

        type="csv",

        help="Upload a CSV file with date and value columns"

    )
    if uploaded_file is not None:

        try:

            # Read the uploaded file

            df = pd.read_csv(uploaded_file)

            st.session_state.data = df
            st.success(" File uploaded successfully!")

            st.subheader("Data Preview")

            st.dataframe(df.head(10))
            # Display basic statistics

            st.subheader(" Dataset Information")

            col1, col2, col3 = st.columns(3)
            with col1:

                st.metric("Total Rows", len(df))

            with col2:

                st.metric("Total Columns", len(df.columns))

            with col3:

                st.metric("Missing Values", df.isnull().sum().sum())
        except Exception as e:

            st.error(f"Error reading file: {str(e)}")
# Data Visualization Section

elif option == "Data Visualization":

    st.header(" Data Visualization")
    if st.session_state.data is not None:

        df = st.session_state.data
        # Column selection

        date_column = st.selectbox("Select Date Column:", df.columns)

        value_column = st.selectbox("Select Value Column:", df.columns)
        if st.button("Generate Visualization"):

            try:

                # Convert date column to datetime

                df[date_column] = pd.to_datetime(df[date_column])
                # Create interactive plot

                fig = px.line(

                    df,

                    x=date_column,

                    y=value_column,

                    title=f"Time Series Plot: {value_column} over {date_column}"

                )
                fig.update_layout(

                    xaxis_title="Date",

                    yaxis_title="Value",

                    hovermode='x unified'

                )
                st.plotly_chart(fig, use_container_width=True)
                # Display trend analysis

                st.subheader(" Trend Analysis")

                col1, col2 = st.columns(2)
                with col1:

                    st.metric(

                        "Average Value",

                        f"{df[value_column].mean():.2f}"

                    )

                    st.metric(

                        "Standard Deviation",

                        f"{df[value_column].std():.2f}"

                    )
                with col2:

                    st.metric(

                        "Minimum Value",

                        f"{df[value_column].min():.2