The AI Forecasting Tool, based on the R Shiny architecture, provides a comprehensive and user-friendly platform for all your data analysis needs. R Shiny, a web application framework for R, allows for the creation of interactive and visually appealing applications without requiring extensive web development skills. Leveraging this powerful framework, users can seamlessly upload their data, make necessary edits, and visualize the information in an intuitive manner. The tool offers a variety of models to choose from, allowing for tailored predictions and forecasts to suit specific requirements. Once the forecasting process is complete, users can easily extract the results for further use. Moreover, the tool includes an interactive AI component that enables discussions on multiple topics, providing valuable insights and guidance throughout the analysis process. This combination of advanced functionality, ease of use, and interactive features makes the AI Forecasting Tool an essential asset for data-driven decision-making.
The top features are as follows -
- Upload your data
- Edit and visualize your data
- Select desired model
- Predict and forecast
- Extract the results
- Discuss multiple topics with AI
This repository contains an AI-based Forecasting Application built with RShiny. The app demonstrates how to leverage AI techniques to perform accurate and interactive time series forecasting. Designed to be intuitive and user-friendly, this RShiny app serves as a powerful tool for exploring predictive analytics and making data-driven decisions.
To run these examples, you'll need:
- R (version 3.5 or above recommended)
- Shiny package installed
- Intermediate knowledge of R programming
- Familiarity with LLM
The code divied into multiple parts. Here's a quick overview of the files -
- ui.R : Contains the UI section of the app
- server.R : Contains all the calculations
- global.R : contiains the app environment details
- functions.R : Required functions are defined here
- helper.R : All the LLM related functions defined here
- mongodb_helper.R : All the functions defined hhere to read/write to Mongo DB
We welcome contributions! If you have additional sample codes or improvements, please:
- Fork this repository.
- Create a feature branch:
git checkout -b feature/your-feature-name- Commit your changes and push the branch:
git push origin feature/your-feature-name- Open a Pull Request.
Make sure your code follows the repository's style and is well-documented.