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Convert CSV data into insights
Causalimpact Web Appp

Causalimpact Web Appp

A web UI for CausalImpact

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What is Causalimpact Web Appp ?

Causalimpact Web Appp is a web-based tool designed to help users analyze and understand causal impact from time series data. It serves as a user-friendly interface for the CausalImpact library, making it accessible through a web browser. The app allows users to upload their data, perform analysis, and visualize results without the need for extensive coding knowledge.

Features

• CSV Data Conversion: Easily convert CSV data into actionable insights. • Time Series Analysis: Perform advanced time series decomposition to identify trends, seasonality, and residuals. • Causal Impact Estimation: Estimate the impact of a specific event or intervention on a time series. • Confidence Intervals: Generate confidence intervals to assess the significance of the estimated impact. • Automated Analysis: Run analyses with default settings or customize parameters for specific use cases. • Data Visualization: Visualize the original data, decomposed components, and estimated impact using interactive plots. • Results Export: Export results in various formats for further analysis or reporting.

How to use Causalimpact Web Appp ?

  1. Access the Web App: Open the Causalimpact Web Appp in your browser.
  2. Upload Your Data: Upload your CSV file containing the time series data.
  3. Select Parameters: Choose the date column, target variable, and the date of the intervention.
  4. Run Analysis: Click to start the analysis and wait for the results.
  5. Interpret Results: Review the output, which includes the estimated impact, confidence intervals, and visualizations.
  6. Export Results: Download the results in the desired format for further use.

Frequently Asked Questions

What file formats are supported?
The app currently supports CSV files only. Ensure your data is in the correct format before uploading.

Can I customize the analysis parameters?
Yes, you can customize parameters such as the model settings, prior scale, and reference period to suit your specific needs.

How do I interpret the results?
The results show the estimated impact of the event on your time series, including confidence intervals. Positive values indicate an increase, while negative values indicate a decrease. Use the visualizations to understand trends and variability.

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