Cash Flow Forecasting Based on Wavelet Transform and Neural Networks

Soylen Z., Mammadova F., Fasounaki M., Ozmen A. I., İnce G.

8th International Conference on Computer Science and Engineering, UBMK 2023, Burdur, Turkey, 13 - 15 September 2023, pp.306-311 identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/ubmk59864.2023.10286711
  • City: Burdur
  • Country: Turkey
  • Page Numbers: pp.306-311
  • Keywords: forecasting, neural networks, time series, wavelet transform
  • Istanbul Technical University Affiliated: Yes


Cash flow forecasting is a critical task for businesses and financial institutions to ensure effective financial planning and decision-making. However, limited data availability poses a significant challenge when developing accurate and robust cash flow prediction models. In this paper, we investigate the performance of various forecasting methods and propose an approach based on wavelet transform for improving the forecasting accuracy. We demonstrate the effectiveness of the proposed approach with the best combination of wavelet functions and methods for forecasting future values in a univariate time series. We investigate the impact of wavelet transform on forecasting techniques based on open-source datasets. Our methodology includes data collection, preprocessing, feature engineering, model selection, and experimentation using different performance evaluation metrics.