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Business KPI Forecasting and Insight Generator

This project direction uses recent research on tabular AI, time-series modelling, natural-language analytics, and LLM-assisted data understanding. The implementation can convert raw data into predictions, insights, visual explanations, and decision-support outputs. The…

Project Overview This project direction uses recent research on tabular AI, time-series modelling, natural-language analytics, and LLM-assisted data understanding. The implementation can convert raw data into predictions, insights, visual explanations, and decision-support outputs. The reference paper, 'Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook', provides the academic base for the topic. Instead of copying the paper abstract directly, this page keeps the same research intent in a safe paraphrased form: the system studies historical trends and predicts future values with charts, metrics, and plain-language insights. The final student implementation can include dataset preparation, model/API integration, dashboard or app interface, result explanation, and a short documentation-ready workflow.
Research Paper Title Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook
Research Paper / PDF Link Open Paper / PDF
Year 2023
Project Area Data Science & Analytics
Project Type Data Project
Required Tools / Software Python, Pandas, NumPy, Scikit-learn, Plotly, Streamlit/Power BI, SQL
Main Features / Working Principle Collect or upload relevant data, preprocess it, apply an AI/ML/LLM/RAG/software workflow, and present the result through a dashboard or application interface for Data Science & Analytics.
Expected Output A working B.Tech project prototype for Data Science & Analytics with input, processing, result display, and explanation/report sections.
Possible Add-ons Admin panel, PDF report export, model comparison, source citations, login system, WhatsApp help button, and deployment on cloud/hosting.
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