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.

翻译：投资专业人员依靠将公司收入外推到未来(即收入预测)来估计规模扩大(在高增长阶段的私营公司)的价值,并通报其投资决定。这是一项手工和实证任务,使预测的质量高度依赖投资专业人员的经验和洞察力。此外,关于规模扩大的财务数据通常是专有的、昂贵的和稀缺的,排除了广泛采用数据驱动的方法。为此,我们提议一种模拟知情的收入外推算(SiRE)算法,在小型数据集和短期系列中产生细微的长期收入预测。SiRE模型将收入动态作为线性动态系统(LDS),使用EM算法解决。主要的创新在于如何在培训和推论期间获得混乱的收入计量。SiRE工作在各部门进行规模扩大,并提供信心估计。关于两项实际任务的数量实验表明,SiRE大大超过我们的基线方法。我们还注意到,SiRE在短时间序列中将一些长期预测作为线性动态动态系统(LDS)模型,而这种预测是使用EM算法解决的。主要创新在于如何在培训和推论期间获得高额收入计量;SiRE工作效率平衡和结果可以解释:SiRE 5的回报。