The purpose of this study is to contrast the forecasting performance of two non-linear models, a regime-switching vector autoregressive model (RS-VAR) and a recurrent neural network (RNN), to that of a linear benchmark VAR model. Our specific forecasting experiment is UK inflation and we utilize monthly data from 1969-2003. The RS-VAR and the RNN perform approximately on par over both monthly a...

ANN modeling is used here to predict missing monthly precipitation data in one station of the eight weather stations network Sulaimani Governorate. Eight models were developed, for each as prediction. The accuracy prediction obtain excellent with correlation coefficients between predicted and measured values ranged from (90% 97.2%). are found after many trials those highest coefficient selected...

Autoregressive Integrated Moving Average (ARIMA) models have been devised for long-term monthly series of maximum and minimum temperatures from south-western Spanish observatories. The original series were transformed into stationary ones, and the orders (p, d, q) for each monthly series were obtained. These were validated using series of residuals, and the parameters of the functions were esti...

Risk analysis in motor insurance aims to identify factors that increase the frequency of accidents. Telematics data is used measure behavioural information drivers. Contextual variables include temperature, rain, wind and traffic conditions are external driver, but may also influence probability having an accident, as well vehicle personal characteristics. This paper uses a monthly panel struct...

Several ANN models were developed to prediction of monthly precipitation data in Mashhad synoptic station. From the total 636 monthly precipitation data (from 1958 to 2008), 580 data has been used for training networks and the rest selected randomly has been used for validation of the models. To extract the precipitation dynamic of this station by ANN, a new approach of three-layer feed-forward...