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

ArXiv ID: 2408.09420 “View on arXiv”

Authors: Unknown

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.

Keywords: GraphRAG, Time Series Forecasting, Venture Capital, Startup Prediction, Network Analysis, Private Equity / Venture Capital

Complexity vs Empirical Score

• Math Complexity: 6.5/10
• Empirical Rigor: 4.0/10
• Quadrant: Lab Rats
• Why: The paper integrates advanced concepts like GraphRAG, knowledge graphs, and multivariate time series (LSTM Seq2Seq) for a novel prediction task, indicating moderate-to-high math complexity. However, the empirical validation relies on news text datasets and described outcomes rather than explicit backtests, metrics, or implementation details, resulting in lower empirical rigor.

  flowchart TD
    subgraph A ["Research Goal"]
        G["Research Goal: Predict VC Startup Success"]
        Q["Q: Can GraphRAG enhance time series models?"]
    end

    subgraph B ["Data & Methodology"]
        D["Input Data: Startup Financials & Network Relationships"]
        M["Method: GraphRAG Augmented Multivariate Time Series Model"]
    end

    subgraph C ["Computational Process"]
        P["Process: Integrate company network data into time series analysis"]
    end

    subgraph D2 ["Outcomes & Findings"]
        R["Result: Enhanced prediction of startup success"]
        F["Finding: Outperforms previous baseline models significantly"]
    end

    G --> Q
    Q --> D
    D --> M
    M --> P
    P --> R
    R --> F