The Seasonal Decomposition of Time Series (STL) Tool is an advanced interactive tool designed to help users decompose time series data into its trend , seasonal , and residual components. STL decomposition is a robust method for analyzing time series data, especially when seasonal patterns are present. This tool uses the STL algorithm to separate these components, enabling users to better understand underlying trends and seasonal effects.

Key Features:

1. Dynamic Input : Users can input their time series data as a comma-separated list and specify the seasonal period.
2. Interactive Visualization : The tool generates separate line charts for the original data, trend, seasonal, and residual components.
3. PDF Download : Export the generated STL decomposition results and visualizations as a PDF for sharing or reporting purposes.
4. Modern Design : Clean and stylish layout with vibrant colors and clear typography for better readability.
5. Self-Contained : The tool operates within its own container, ensuring it doesn’t interfere with the page header or footer.

Use Cases:

• Trend Analysis : Analysts can use this tool to identify long-term trends in time series data.
• Seasonality Detection : Researchers can isolate and analyze seasonal patterns in datasets such as sales, weather, or stock prices.
• Residual Analysis : Professionals can examine residuals to detect anomalies or irregularities in the data.
• Reporting : Users can export STL decomposition charts to include in reports or presentations.

How It Works:

1. Introduction : STL decomposition breaks down a time series into three components:
   • Trend : The long-term progression of the data.
   • Seasonal : The repeating short-term cycles or patterns.
   • Residual : The remaining noise or irregularities after removing the trend and seasonal components.
2. Input Fields : Users enter their time series data as a comma-separated list and specify the seasonal period (e.g., 12 for monthly data with yearly seasonality).
3. Visualization : The tool generates line charts for the original data, trend, seasonal, and residual components.
4. Export : Users can download the STL decomposition results and visualizations as a PDF for further use.