Forecaster
A Specialized, Customizable, and Secure self-service tool
Time series forecasting has applications across domains and industries, especially in healthcare, but the technical expertise required to analyze data, build models, and interpret results can be a barrier to using these techniques. Forecaster presents a web platform that makes the process of analyzing and plotting data, training forecasting models, and interpreting and viewing results accessible to researchers and clinicians. Users can upload data and generate plots to showcase their variables and the relationships between them. The platform supports multiple forecasting models and training techniques, which are highly customizable according to the user’s needs. Additionally, recommendations and explanations can be generated from a large language model that can help the user choose appropriate parameters for their data and understand the results for each model. The goal is to integrate this platform into learning health systems for continuous data collection and inference from clinical pipelines.
Why use Forecaster?
By removing the complexity of data analysis expertise, Forcaster makes time-series forecasting much easier and more accessible, especially if you don’t have deep technical expertise. Forecaster efficiently visualizes trends and relationships in the uploaded data and offers multiple customizable forecasting models. This allows users to tailor their results to their specific needs. By providing AI-generated recommendations and explanations of the uploaded data, Forecaster helps users choose their settings and understand the outputs. Being accessible for healthcare use supports active research and continuous learning from clinical data.
Aaron Mullen presented LLM Factory, An Institutional Platform for Secure Self-Service LLM Exploration, at the March 2025 AMIA Informatics Summit in Pittsburgh, PA.
