Forecasting of Financial Series for the Nevada Department of Transportation Using Deterministic and Stochastic Methodologies

In this research, a set of financial series was forecasted using data from the Nevada Department of Transportation (NDOT) with the objective of facilitating financial management and associated decision-making. Both deterministic and stochastic methods for seven financial time series, which were independent and univariate, from NDOT's financial data warehouse. Data from 2001 to 2014 were annually and equally spaced. The data series included drivers’ license fees, federal aid revenue, gas tax revenue, motor carrier fees, registration fees, special fuel tax revenue, and total state revenue. The deterministic forecasting methods used included Simple, Holt, Brown, and Damped Trend, and the stochastic forecast used ARIMA (p, d, q). The Simple and Holt methods provided an adequate forecast for 28% of the cases. Brown's method provided an adequate forecast for 44% of the cases. However, the stochastic process, the ARIMA method, did not find an acceptable goodness of fit. An absence of large datasets likely precluded an appropriate estimation when using the ARIMA (p, d, q) method. Tests were performed using functional curves including linear, logarithmic, inverse, quadratic, cubic, growth-exponential, and logistic. The best fits were obtained using the cubic functional form, with an average coefficient of determination of 82%.