Time series forecasting using machine learning algorithms has gained popularity recently. Random forest is a machine learning algorithm implemented in time series forecasting; however, most of its forecasting properties have remained unexplored. Here we focus on assessing the performance of random forests in one-step forecasting using two large datasets of short time series with the aim to sugg...

Abstract: Computational intelligence approaches have gradually established themselves as a popular tool for forecasting the complicated financial markets. Forecasting accuracy is one of the most important features of forecasting models; hence, never has research directed at improving upon the effectiveness of time series models stopped. Nowadays, despite the numerous time series forecasting mod...

Accurate prediction of the future electricity consumption is crucial for production electricity management. Since the storage of electrical energy is very difficult, reliable and accurate prediction of power consumption is important. Different approaches for this purpose were used. In this paper, Grey model (1,1) based on grey system theory has been used for forecasting results. Annual electric...

Traditionally, the quality of a forecasting model is judged by how it compares, in terms of accuracy, to alternative models. However, by providing a relative measure, no indication is given as to how much scope there might be for improvements beyond the benchmark model. When judgemental methods are used alongside simple forecasting models, the scope for such improvements is considerable and dif...

In recent decades artificial neural networks (ANNs) have shown great ability in modeling and forecasting non-linear and non-stationary time series and in most of the cases especially in prediction of phenomena have showed very good performance. This paper presents the application of artificial neural networks to predict drought in Yazd meteorological station. In this research, different archite...

Undesired radiation exposure to caregivers is an important problem in radioiodine therapy in thyroid carcinoma patients, especially when patient is non-ambulatory. Special precautions are needed to keep the exposure to caregivers low when higher dose of radioiodine is necessary. Here we present a case-report of a 74-year old male patient with papillary thyroid carcinoma with local invasion and...

I n this paper, we specify that the GARCH(1,1) model has strong forecasting volatility and its usage under the truncated standard normal distribution (TSND) is more suitable than when it is under the normal and student-t distributions. On the contrary, no comparison was tried between the forecasting performance of volatility of the daily return series using the multi-step ahead forec...

Abstract In social interactions, people often reason about the beliefs, goals and intentions of others. This theory mind allows them to interpret behavior others, predict how they will behave in future. People can also use this ability recursively: higher-order abilities as “he thinks that I don’t know he sent me an anonymous letter”. Previous agent-based modeling research has shown usefulness ...

The increasing interest aroused by more advanced forecasting techniques, together with the requirement for more accurate forecasts of tourism demand at the destination level due to the constant growth of world tourism, has lead us to evaluate the forecasting performance of neural modelling relative to that of time series methods at a regional level. Seasonality and volatility are important feat...