Biçen, Yunus2020-05-012020-05-0120171300-70092147-5881https://doi.org/10.5505/pajes.2016.69335https://hdl.handle.net/20.500.12684/6037WOS: 000443167300012Adaptive smoothing methods were suggested to improve forecast results on the characteristic changes of time series. The existing adaptive smoothing methods have been diversified over the years. Many of them are comprised of complicated logical or mathematical propositions for improving forecast accuracy, which are very different from the original simple method called Trigg and Leach method. A new method named Fuzzy Tuning Exponential Smoothing is introduced in this paper introduces. This method is successful in improving the forecast accuracy, especially for the time series including level shift or level shift with outlier deflection. The empirical application carried out on 'The M2-Competition Time Series'. The statistical analysis results demonstrate that the method outperforms classical adaptive smoothing method in terms of forecasting accuracy. In addition, the proposed method is relatively simple compared to other advanced adaptive methods.en10.5505/pajes.2016.69335info:eu-repo/semantics/openAccessAdaptive exponential smoothingDeflectionForecastingFuzzy logicLevel shiftTime seriesArticle2318894N/A