AI Summary & Key Takeaways
AI InsightIn this sense, the results are compared to the results of SETAR model and it’s seen that Tsaur’s approach has provided better results compared to the forecasts of typical time series
📌 3 Key Takeaways
Abstract
Modeling of time series with fuzzy logic has found an increasingly expanding usage in recent years. One of the most important reasons for thisis that fuzzy logic approach doesn’t require assumptions needed by the typical time series. Inclusion of the some weightings and probability calculations at the forecast stage into the first studies starting through the modeling of time series with fuzzy logic resulted in further improvement of the forecast quality. Tsaur(2011) achieved better forecasted results by including the Markov transition probabilities matrix. Fuzzy time series is also an approach which can be *exibly used in various model structures as it easily overcomes the difficulties causedby the model structure - linear or non-linear form. In this study, Markov method of Tsaur is applied on the monthly capacity utilization ratio (CUR)of Turkey which has a non-linear structure and free of seasonality belongingto the period between 2007-2015. In this sense, the results are compared to the results of SETAR model and it’s seen that Tsaur’s approach has provided better results compared to the forecasts of typical time series