Cash Flow Forecasting Using Time Series Analysis, Machine Learning and Deep Learning Models: Evidence From New York City Financial Data
Type: article
Abstract: Accurate cash flow forecasting is a cornerstone of liquidity risk management, yet identifying the optimal model for small, seasonal datasets remains a challenge. This study conducts a comparative performance analysis of four distinct modeling paradigms: Autoregressive (AR), Seasonal Autoregressive Integrated Moving Average (SARIMA), Support Vector Regression (SVR), and a Hybrid LSTM-GRU neural network. Using a monthly cash flow dataset characterized by 3-month seasonality, the models were evaluated on predictive accuracy RMSE, MAE, goodness-offit (R2), and computational efficiency. The results indicate a clear hierarchy in model performance. While the SVR model achieved the lowest RMSE (16,096) and the fastest fitting time (0.0011s), the SARIMA(2,0,0)(0,0,1)[3] model emerged as the most effective tool for practical financial application, delivering the highest R2 (0.79) and the lowest MAE (11,688). Despite the increased computational complexity of the Hybrid LSTM-GRU and SARIMA architectures, the findings suggest that for small-scale financial time series, the statistical precision and seasonal-handling capabilities of SARIMA outweigh the marginal speed advantages of machine learning alternatives.
Source: CyberLeninK (CyberLeninka)
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7168226402