Research paper on A Comparison of Mother Wavelet Functions and Their Effect on Daily Balance Forecast Accuracy in the Treasury Single Account

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Title: A Comparison of Mother Wavelet Functions and Their Effect on Daily Balance Forecast Accuracy in the Treasury Single Account

Abstract: Achieving government payment obligations, reducing the cost of maintaining the cash reserve, ensuring the absence of outstanding debt accumulation, and ensuring investment in financial instruments to generate additional income all depend on the TSA’s ability to forecast cash flow accurately. By using a discrete wavelet transform to decompose the time series derived from the daily remaining balances in the TSA, this study seeks to increase the precision of conventional forecasting techniques. The accuracy of time series forecasts compiled from the daily remaining balances in the TSA is compared to the traditional forecasting method without prior timeseries decomposition using a comparison of the effects of choosing a mother wavelet out of 570 mother wavelet functions (gonal Spline; Reverse Biorthogonal Spline; Meyer; Shannon; Battle-Lemarie; and Cohen-Daubechies-Feauveau) and the decomposition level (from 1 to 8). In comparison to conventional forecasting models, the model with prior time series decomposition based on the Reverse Biorthogonal Spline Wavelet [5.5] mother wavelet function has the highest accuracy after eight iterations. The mother wavelet selection and decomposition level have a significant impact on how accurately the TSA forecasts the daily remaining balances.

Keywords: wavelet analysis; discrete wavelet transform; treasury single account; balances; time series forecasting

Paper Quality: SCOPUS / Web of Science Level Research Paper

Subject: Economics

Writer Experience: 20+ Years

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