The Cross-Correlation Function (CCF) Calculator is an interactive tool designed to help users analyze the relationship between two time series datasets. The cross-correlation function measures the similarity between two series as a function of the lag applied to one of them. This tool is particularly useful for identifying lead-lag relationships, synchronicity, or delays between two datasets.

Key Features:

1. Dynamic Input : Users can input two time series datasets as comma-separated lists, and the tool will compute the cross-correlation function.
2. Interactive Visualization : A bar chart displays the cross-correlation values at different lags, highlighting significant correlations.
3. PDF Download : Export the generated cross-correlation results and visualization 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:

• Lead-Lag Analysis : Researchers can identify whether one time series leads or lags another, such as stock prices or economic indicators.
• Signal Processing : Engineers can use this tool to analyze relationships between signals in fields like audio processing or sensor data.
• Reporting : Professionals can export cross-correlation charts to include in reports or presentations.

How It Works:

1. Introduction : The cross-correlation function measures the correlation between two time series as one series is shifted (lagged) relative to the other. Positive lags indicate the second series is delayed relative to the first, while negative lags indicate the first series is delayed.
2. Input Fields : Users enter two time series datasets as comma-separated lists.
3. Visualization : The tool generates a bar chart displaying the cross-correlation values at different lags.
4. Export : Users can download the cross-correlation results and visualization as a PDF for further use.