A web UI for CausalImpact
Load and analyze CSV data using Pandas
A unified service, "EveryChat," that allows you to choose an
Analyze a PDF and extract key insights with citations
Analyze data to extract meaningful insights
Analyze data and generate insights using XGBoost
Need to analyze data? Let a Llama-3.1 agent do it for you!
Visualize and analyze data using a CSV file
Analyse your CSV data
Predict crop yields based on weather, soil conditions, and a
testing
Analyze educational data to discover insights
Accepts CSV, XLS, XLSX, JSON, XML, TXT. Try our demo!
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.
• 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.
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.