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

TL;DR

This paper introduces a GraphRAG-augmented multivariate time series model that incorporates inter-company relationships to improve startup success predictions in venture capital, outperforming previous methods.

Contribution

The paper presents a novel GraphRAG-augmented approach that integrates inter-company relationships into time series analysis for better startup success prediction.

Findings

Model significantly outperforms previous methods

Incorporating relationships improves prediction accuracy

Enhanced understanding of startup ecosystem dynamics

Abstract

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.