Article · CyberLeninK

CASH FLOW FORECASTING USING TIME SERIES ANALYSIS, MACHINE LEARNING AND DEEP LEARNING MODELS: EVIDENCE FROM NEW YORK CITY FINANCIAL DATA

Nurdaulet Kanatuly Sharmetov, Amandyk Zhankozhauly Kartbayev

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.

Forecasting Techniques and ApplicationsStock Market Forecasting MethodsTime Series Analysis and Forecastingcash flow forecastingliquidity risk managementSARIMA

CASH FLOW FORECASTING USING TIME SERIES ANALYSIS, MACHINE LEARNING AND DEEP LEARNING MODELS: EVIDENCE FROM NEW YORK CITY FINANCIAL DATA

Abstract

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