On the Links Between Forecasting Performance and Statistical Features of Time Series Applied to the Cash Flow of Self-Employed Workers?

Abstract

Proper cash ow forecasting is a complex task that can be done by modelling the cash ow data as a time series. Although parametric methods have been widely used to accomplish this task, they require some assumptions about the data that are di cult to hold. A well-founded alternative is the use of fuzzy inference systems, which have proven to be competitive in many practical problems. This paper presents a statistical study that compares the performance of fuzzy inference forecasting systems with that of a traditional parametric approach, in a cashflow forecasting problem based on the weekly income and expense data of 340 self-employed workers over a period of 338 weeks with four diferent time horizons (1, 4, 9 and 13 weeks). We also check for significant links between several statistical characteristics and observed performance, to determine which features might most a ect the quality of the predictions. After finding that kurtosis is the most correlated feature, a more detailed exploration is performed on it.