Hybrid LSTM-ARIMA modeling for accurate financial forecasting in nonprofit fundraising

Financial forecasting is an important element of strategic planning in a nonprofit organization since the pattern of donations is typically intermittent and seasonal, and also subject to various socio-economic factors. ARIMA and other classical time series models are good at time dependencies that are linear but not nonlinear and complex, like fundraising data. LSTM networks, on the other hand, are excellent at nonlinear dynamics, but fail to detect underlying statistical dynamics. Thus, the suggested paper is an LSTM-ARIMA model that would integrate both of these models to create a high degree of forecasting precision. ARIMA is deployed to model the linear trends and the residuals of the past data on the donations, and LSTM networks are trained to predict the nonlinear patterns based on the residual data. The final prediction is one that is a blend of models. The proposed hybrid model achieves a reduction in RMSE and MAE by approximately 18% compared to the ARIMA baseline, improving predictive performance on donor behavior, which suggests the hybrid model is capable of enhancing nonprofit fundraising predictions.