# Simulation-Informed Revenue Extrapolation with Confidence Estimate for Scaleup Companies Using Scarce Time-Series Data

arXiv: https://arxiv.org/abs/2208.10375 · HTML: https://mlsanity.com/paper/2208.10375/

## Key facts

- Cited by 5 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 19 Aug 2022.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://mlsanity.com/paper/2012.07436/) (2020)
- [DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks](https://mlsanity.com/paper/1704.04110/) (2017)
- [Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting](https://mlsanity.com/paper/1907.00235/) (2019)
- [Dilated Residual Networks](https://mlsanity.com/paper/1705.09914/) (2017)
- [Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019](https://mlsanity.com/paper/1911.13288/) (2019)
- [Online Advertising Revenue Forecasting: An Interpretable Deep Learning Approach](https://mlsanity.com/paper/2111.08840/) (2021)
- [A Generalized Flow for B2B Sales Predictive Modeling: An Azure Machine Learning Approach](https://mlsanity.com/paper/2002.01441/) (2020)
- [Analyzing Linear Dynamical Systems: From Modeling to Coding and Learning](https://mlsanity.com/paper/1608.01059/) (2016)
- [Large-scale Uncertainty Estimation and Its Application in Revenue Forecast of SMEs](https://mlsanity.com/paper/2005.00718/) (2020)

## Built on by (most cited first, 5 of 5)

- [Self-Supervised Learning of Time Series Representation via Diffusion Process and Imputation-Interpolation-Forecasting Mask](https://mlsanity.com/paper/2405.05959/) (2024)
- [Label Inference Attack against Split Learning under Regression Setting](https://mlsanity.com/paper/2301.07284/) (2023)
- [Using Deep Learning to Find the Next Unicorn: A Practical Synthesis](https://mlsanity.com/paper/2210.14195/) (2022)
- [Enhancing Startup Success Predictions in Venture Capital: A GraphRAG Augmented Multivariate Time Series Method](https://mlsanity.com/paper/2408.09420/) (2024)
- [Beyond Gut Feel: Using Time Series Transformers to Find Investment Gems](https://mlsanity.com/paper/2309.16888/) (2023)

## Abstract (arXiv)

Investment professionals rely on extrapolating company revenue into the future (i.e. revenue forecast) to approximate the valuation of scaleups (private companies in a high-growth stage) and inform their investment decision. This task is manual and empirical, leaving the forecast quality heavily dependent on the investment professionals' experiences and insights. Furthermore, financial data on scaleups is typically proprietary, costly and scarce, ruling out the wide adoption of data-driven approaches. To this end, we propose a simulation-informed revenue extrapolation (SiRE) algorithm that generates fine-grained long-term revenue predictions on small datasets and short time-series. SiRE models the revenue dynamics as a linear dynamical system (LDS), which is solved using the EM algorithm. The main innovation lies in how the noisy revenue measurements are obtained during training and inferencing. SiRE works for scaleups that operate in various sectors and provides confidence estimates. The quantitative experiments on two practical tasks show that SiRE significantly surpasses the baseline methods by a large margin. We also observe high performance when SiRE extrapolates long-term predictions from short time-series. The performance-efficiency balance and result explainability of SiRE are also validated empirically. Evaluated from the perspective of investment professionals, SiRE can precisely locate the scaleups that have a great potential return in 2 to 5 years. Furthermore, our qualitative inspection illustrates some advantageous attributes of the SiRE revenue forecasts.