Neutrosophic Statistics for Enhanced Time Series Analysis of Unemployment Trends in Ecuador

This study harnesses advanced time series models ARIMA, ETS, and SARIMA, coupled with neutrosophic statistics, to forecast unemployment trends through interval-based predictions. Transforming these predictions into neutrosophic forms enables the quantification of indeterminacy, providing a nuanced interpretation of potential economic scenarios. The integration of neutrosophic statistics enhances the interpretative power and accuracy of these models, offering a deeper insight into the inherent uncertainties of economic forecasting. The approach reveals not only the variabilities and potential outcomes within the unemployment rates but also strengthens the decision-making processes by presenting data that encompass both precision and indeterminacy. This paper underscores the importance of advanced statistical methods in economic predictions, suggesting further exploration into other economic metrics and advocating for a broader application of neutrosophic statistics to enhance the reliability of economic forecasting across diverse contexts. Keywords: Neutrosophic Statistics, Time Series Forecasting, Economic Forecasting, Uncertainty Quantification.