Triple Simplex Matrix Completion for Expense Forecasting
This addresses expense forecasting for businesses, but it is incremental as it builds on existing matrix completion methods with specific constraints.
The paper tackles the problem of forecasting project expenses to prevent budget overruns by proposing a constrained non-negative matrix completion model that learns expense patterns in latent space and guarantees budget constraints without post-processing. Results on two real datasets show effectiveness compared to state-of-the-art algorithms.
Forecasting project expenses is a crucial step for businesses to avoid budget overruns and project failures. Traditionally, this has been done by financial analysts or data science techniques such as time-series analysis. However, these approaches can be uncertain and produce results that differ from the planned budget, especially at the start of a project with limited data points. This paper proposes a constrained non-negative matrix completion model that predicts expenses by learning the likelihood of the project correlating with certain expense patterns in the latent space. The model is constrained on three probability simplexes, two of which are on the factor matrices and the third on the missing entries. Additionally, the predicted expense values are guaranteed to meet the budget constraint without the need of post-processing. An inexact alternating optimization algorithm is developed to solve the associated optimization problem and is proven to converge to a stationary point. Results from two real datasets demonstrate the effectiveness of the proposed method in comparison to state-of-the-art algorithms.