The problem of fuzzy time series forecasting plays an important role in many scientific areas such as statistics and neural networks. While forecasting fuzzy time series, most of forecasting applications use the same length of intervals. The determination of length of intervals is significant and critical in fuzzy time series forecasting. The usage of convenient performance measure may also hav...

Cointegration has different theoretical implications for forecasting. Several empirical studies have compared the out of sample forecasting performance of cointegrted VECMs against unrestricted VARs in levels and in differences. The results of these studies have been generally mixed and inconclusive. This paper provides a comprehensive review over the subject, and also examines the effects...

in recent years, various time series models have been proposed for financial markets forecasting. in each case, the accuracy of time series forecasting models are fundamental to make decision and hence the research for improving the effectiveness of forecasting models have been curried on. many researchers have compared different time series models together in order to determine more efficient ...

Although over a thousand scientific papers address the topic of load forecasting every year, only a few are dedicated to finding a general framework for load forecasting that improves the performance, without depending on the unique characteristics of a certain task such as geographical location. Meta-learning, a powerful approach for algorithm selection has so far been demonstrated only on uni...

Short term load forecasting (STLF) plays an important role in the economic and reliable operation ofpower systems. Electric load demand has a complex profile with many multivariable and nonlineardependencies. In this study, recurrent neural network (RNN) architecture is presented for STLF. Theproposed model is capable of forecasting next 24-hour load profile. The main feature in this networkis ...

Fuzzy time series model has been developed to either improve forecasting accuracy or reduce computation time, whereas a residul analysis in order to improve its forecasting performance is still lack of consideration. In this paper, we propose a novel Fourier method to revise the analysis of residual terms, and then we illustrate it to forecast the Japanese tourists visiting in Taiwan per year. ...

Hybrid model is a popular forecasting model in renewable energy related forecasting applications. Wind speed forecasting, as a common application, requires fast and accurate forecasting models. This paper introduces an Empirical Mode Decomposition (EMD) followed by a k Nearest Neighbor (kNN) hybrid model for wind speed forecasting. Two configurations of EMD-kNN are discussed in details: an EMD-...

  In recent years, various time series models have been proposed for financial markets forecasting. In each case, the accuracy of time series forecasting models are fundamental to make decision and hence the research for improving the effectiveness of forecasting models have been curried on. Many researchers have compared different time series models together in order to determine more efficien...

In this paper we investigate the forecasting performance of the non-linear time series SETAR model by using Canadian GDP data from 1965 to 2000. Besides the with-insample fit, the forecasting performance of a standard linear ARIMA model for the same sample has also been generated for comparative purposes. Two forecasting methods, 1step-ahead and multi-step-ahead forecasting are compared for eac...