Best Time Series Forecasting Web App using Streamlit
Time Series Forecasting
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:
| Project Name | Language Used | Developer |
|---|---|---|
| Time Series Forecasting | Python | UPDATEGADH |
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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 features:
- Upload time series datasets in CSV format
- Interactive visualization with Plotly
- Data preprocessing & validation utilities
- Forecasting using:
- ARIMA
- LSTM
- Prophet
- Model performance evaluation & comparison
- Clean UI built with Streamlit
- No external database needed — works entirely with uploaded data
How It Works
Once deployed, users can upload their time-series dataset in CSV format. The app automatically preprocesses and validates the data. Then, users can select forecasting models and visualize the results directly in the browser.
Each model generates future values, plots, and metrics, allowing a direct comparison between model performances.
Run Instructions
This is a standalone Python Streamlit app. To run the project:
- Extract the ZIP file.
- Open terminal in the extracted folder.
- Install dependencies:
pip install -r requirements.txt - Launch the app:
streamlit run app.py
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