Aims & Scope
The Turkish Journal of Forecasting (TJF), as an international and multidisciplinary journal, focuses on forecasting methods. The aim of the TJF is to procure a platform to integrate the research subjects and fields, and to bridge over the between theory and practice dealing with any aspect of forecasting. For this purpose, the TJF publishes high quality refereed papers for the benefit of decision and policy makers. The journal slots in research papers including theoretical, practical, computational and methodological research and procedures of enhancement the practice of forecasting. The journal is accessible to various perspectives and to constitute the discussion platform to achieve new solutions for facing the real-world forecasting problems. The journal also welcomes the papers dealing with the new concepts of modelling and the contemporary computing intensive forecasting systems on top of the conventional forecasting models in decision-making processes.
Topics covered in the Turkish Journal of Forecasting (it is not bounded):
- Business forecasting
- Demographic forecasting
- Energy forecasting
- Environmental forecasting
- Financial forecasting
- Legal and judgement forecasting
- Political forecasting
- Technological forecasting
And also the methods covered in the Turkish Journal of Forecasting (it is not bounded):
- The probabilistic and conventional methods
- ARIMA and related models
- ARCH and related models
- Bayesian forecasting models
- Bilinear methods
- Filters and smoothing methods
- Nonlinear time series forecasting models
- Threshold methods
- Time series regression
- The others
- Fuzzy Inference Systems
- ANFIS
- Fuzzy functions approaches
- Fuzzy regression techniques
- Fuzzy arithmetic based methods
- Fuzzy time series (FTS) forecasting models
- Artificial intelligence optimization based FTS forecasting models
- Artificial neural networks based FTS forecasting models
- Clustering based FTS forecasting models
- Artificial Neural Networks (ANNs)
- Different neuron models based ANNs
- Feedforward ANNs
- High order ANNs
- Recurrent ANNs
- Robust learning algorithms based ANNs
- Hybrid Models using computational and probabilistic models
- Forecast combination based forecasting methods
- Ensemble techniques based forecasting methods
- Bootstrap aggregation and other resampling based forecasting methods
The paper types of the TJF include research article (theoretical and practical), review articles, editorials and letters.