EVALUATION OF THEILS U: A NAÏVE FORECAST APPLICATION
Keywords:
forecast, Naïve, accuracy, error, Theil’s UAbstract
The reliability of any forecast needs to be tested effectively with an empirical data. Simple or complicated forecast methods have many a time failed subjected to empirical examination. There is no agreement among scholars as to which metric is the best for determining the best forecasting method. So this paper evaluates the basic of forecast techniques of predicting the future values and comparing its accuracy by Theil’s U statistic. The predicted values were estimated by Naïve’s method and the errors are calculated to verify the accuracy of the forecasted values as well. The testing has been done with a set of fictitious data set which helps to explain the steps in establishing the accuracy of the projected model.
References
Akpinar, M., Yumuşak, N. (2017): Naive forecasting of household natural gas consumption with sliding window approach. – Turkish Journal of Electrical Engineering & Computer Sciences 25(1): 30-45.
Armstrong, J.S. (2001): Selecting forecasting methods. – In Principles of Forecasting, Springer 21p.
Armstrong, J.S., Collopy, F. (2000): Another Error Measure for Selection of the Best Forecasting Method: The Unbiased Absolute Percentage Error. – International Journal of Forecasting 8(2): 69-80.
Brockwell, P.J., Davis, R.A. (2016): Introduction to time series and forecasting. – Springer 437p.
Chen, A.S., Leung, M.T. (2003): A Bayesian vector error correction model for forecasting exchange rates. – Computers & Operations Research 30(6): 887-900.
Green, K.C., Armstrong, J.S. (2015): Simple versus complex forecasting: The evidence. – Journal of Business Research 68(8): 1678-1685.
Hyndman, R.J., Athanasopoulos, G. (2013): Forecasting: Principles and practice. – OTexts 292p.
Hyndman, R.J., Koehler, A.B. (2006): Another look at measures of forecast accuracy. – International Journal of Forecasting 22(4): 679-688.
Real Statistics using Excel (2021): Time series forecast error. – Real Statistics using Excel Official Portal. Available on:
Ribeiro, R.C.M., Quadros, T.A., Saldarriaga, J.J., Júnior, S., de Almeida, J.F., Marques, G.T. (2019): Forecasting incidence of tuberculosis cases in Brazil based on various univariate time-series models. – International Journal for Innovation Education and Research 7(1): 894-909.
Russell, T.D., Adam Jr, E.E. (1987): An empirical evaluation of alternative forecasting combinations. – Management Science 33(10): 1267-1276.
Siraj-Ud-Doulah, M. (2019): Time Series Forecasting: A Comparative Study of VAR ANN and SVM Models. – Journal of Statistical and Econometric Methods 8(3): 21-34.