# Enhancing Startup Success Predictions in Venture Capital: A GraphRAG Augmented Multivariate Time Series Method

arXiv: https://arxiv.org/abs/2408.09420 · HTML: https://mlsanity.com/paper/2408.09420/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 40 references that are in the map.
- First posted on arXiv 18 Aug 2024.

## Builds on (most cited first, 10 of 10)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [From Local to Global: A Graph RAG Approach to Query-Focused Summarization](https://mlsanity.com/paper/2404.16130/) (2024)
- [DeepSeek LLM: Scaling Open-Source Language Models with Longtermism](https://mlsanity.com/paper/2401.02954/) (2024)
- [From Louvain to Leiden: guaranteeing well-connected communities](https://mlsanity.com/paper/1810.08473/) (2018)
- [BBT-Fin: Comprehensive Construction of Chinese Financial Domain Pre-trained Language Model, Corpus and Benchmark](https://mlsanity.com/paper/2302.09432/) (2023)
- [WanJuan: A Comprehensive Multimodal Dataset for Advancing English and Chinese Large Models](https://mlsanity.com/paper/2308.10755/) (2023)
- [SSFF: Investigating LLM Predictive Capabilities for Startup Success through a Multi-Agent Framework with Enhanced Explainability and Performance](https://mlsanity.com/paper/2405.19456/) (2024)
- [Graph Neural Network Based VC Investment Success Prediction](https://mlsanity.com/paper/2105.11537/) (2021)
- [CompanyKG: A Large-Scale Heterogeneous Graph for Company Similarity Quantification](https://mlsanity.com/paper/2306.10649/) (2023)
- [Simulation-Informed Revenue Extrapolation with Confidence Estimate for Scaleup Companies Using Scarce Time-Series Data](https://mlsanity.com/paper/2208.10375/) (2022)

## Built on by (most cited first, 2 of 2)

- [Enhancing Large Language Models (LLMs) for Telecommunications using Knowledge Graphs and Retrieval-Augmented Generation](https://mlsanity.com/paper/2503.24245/) (2025)
- [Beyond Isolated Investor: Predicting Startup Success via Roleplay-Based Collective Agents](https://mlsanity.com/paper/2512.22608/) (2025)

## Abstract (arXiv)

In the Venture Capital (VC) industry, predicting the success of startups is challenging due to limited financial data and the need for subjective revenue forecasts. Previous methods based on time series analysis often fall short as they fail to incorporate crucial inter-company relationships such as competition and collaboration. To fill the gap, this paper aims to introduce a novel approach using GraphRAG augmented time series model. With GraphRAG, time series predictive methods are enhanced by integrating these vital relationships into the analysis framework, allowing for a more dynamic understanding of the startup ecosystem in venture capital. Our experimental results demonstrate that our model significantly outperforms previous models in startup success predictions.