In this paper Semi-Markov models are used to forecast the triple dimensions of next earthquake occurrences. Each earthquake can be investigated in three dimensions including temporal, spatial and magnitude. Semi-Markov models can be used for earthquake forecasting in each arbitrary area and each area can be divided into several zones. In Semi-Markov models each zone can be considered as a sta...

Signal detection theory (SDT) asserts that sensory analysis is limited only by noise, and not by the number of stimuli analysed. To test this claim, we measured the accuracy of visual search for a single tilted element (the target) among 7 horizontal elements (distractors) using several different exposure durations, each terminated by a random noise mask. In the uncued condition, each element w...

We consider the reasons for nowcasting, the timing of information and sources thereof, especially contemporaneous data, which introduce different aspects compared to forecasting. We allow for the impact of location shifts inducing nowcast failure and nowcasting during breaks, probably with measurement errors. We also apply a variant of the nowcasting strategy proposed in Castle and Hendry (2009...

There is a wealth of literature documenting the biases and errors associated with judgmentbased forecasting—c.f. (McGlothlin 1956, Tversky and Kahneman 1974, Wright and Ayton 1986, Bolger and Harvey 1998), for example. Mentzer and Bienstock (1998) and Tyebjee (1987) point out that in addition to these problems, judgmental sales forecasts could be distorted by other factors, such as organization...

A set of rigorous diagnostic techniques is used to evaluate the forecasting performance of five multivariate time-series models for the U.S. cattle sector. The root-meansquared-error criterion along with an evaluation of the rankings of forecast errors reveals that the Bayesian vector autoregression (BVAR) and the unrestricted VAR (UVAR) models generate forecasts which are superior to both a re...

A novel high-order fuzzy time series model for stock price forecasting is presented based on the fuzzy cmeans (FCM) discretization method and artificial neural networks (ANN). In the proposed model, the FCM discretization method obtained reliable interval lengths. In addition, the fuzzy relation matrix was obtained from ANN, mooting the need for complex and time-consuming matrix operations. The...

Neural based geomagnetic forecasting literature has heavily relied upon non-sequential algorithms for estimation of model parameters. This paper proposes sequential Bayesian recurrent neural filters for online forecasting of the Dst index. Online updating of the RNN parameters allows for newly arrived observations to be included into themodel. The online RNN filters are compared to two (non-seq...

This paper presents an artificial neural network(ANN) approach to electric load forecasting. The ANN is used to learn the relationship among past, current and future temperatures and loads. In order to provide the forecasted load, the ANN interpolates among the load and temperature data in a training data set. The average absolute errors of the one-hour and 24-hour ahead forecasts in our test o...