The Seasonal Trend Analysis Tool is an interactive tool designed to help users analyze and visualize seasonal trends in time series data. This tool decomposes the data into its seasonal , trend , and residual components, enabling users to identify patterns, long-term trends, and irregularities. The analysis is particularly useful for datasets with periodic fluctuations, such as sales, weather, or stock prices.

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. Customizable Period : Users can define the length of the seasonal cycle (e.g., 12 for monthly data with yearly seasonality).
4. PDF Download : Export the generated seasonal trend analysis results and visualizations as a PDF for sharing or reporting purposes.
5. Modern Design : Clean and stylish layout with vibrant colors and clear typography for better readability.
6. 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 seasonal trend analysis charts to include in reports or presentations.

How It Works:

1. Introduction : Seasonal trend analysis 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 seasonal trend analysis results and visualizations as a PDF for further use.