This study presents a new simple approach for combining empirical with raw (i.e., not bias corrected) coupled model ensemble forecasts in order to make more skillful interval forecasts of ENSO. A Bayesian normal model has been used to combine empirical and raw coupled model December SST Niño-3.4 index forecasts started at the end of the preceding July (5-month lead time). The empirical forecast...

We revisit the concept of unpredictability to explore its implications for forecasting strategies in a non-stationary world subject to structural breaks, where model and mechanism differ. Six aspects of the role of unpredictability are distinguished, compounding the four additional mistakes most likely in estimated forecasting models. Structural breaks, rather than limited information, are the ...

Recently, it was shown that calibration with an error less than δ > 0 is almost surely guaranteed with a randomized forecasting algorithm, where forecasts are chosen using randomized rounding up to δ of deterministic forecasts. We show that this error can not be improved for a large majority of sequences generated by a probabilistic algorithm: we prove that combining outcomes of coin-tossing an...

This article compares the accuracy of vector autoregressive (VAR), restricted vector autoregressive (RVAR), Bayesian vector autoregressive (BVAR), vector error correction (VEC) and Bayesian vector error correction (BVEC) models in forecasting the exchange rates for ve Central and Eastern European currencies (Czech Koruna, Hungarian Forint, Polish Zloty, Slovak Koruna and Slovenian Tolar) agains...

Driven by a trend towards renewable forms of generation, in particular wind, the nature of power system operation is changing. With respect to wind power, the uncertainty of the wind becomes an issue which must be considered. Through the use of wind forecasting, this uncertainty may be managed. The error inherent in forecasting will impact system reliability and cost as will inaccuracies in ass...

this paper presents the prediction of vehicle's velocity time series using neural networks. for this purpose, driving data is firstly collected in real world traffic conditions in the city of tehran using advance vehicle location devices installed on private cars. a multi-layer perceptron network is then designed for driving time series forecasting. in addition, the results of this study a...

This paper presents the applicability of artificial neural networks for 24 hour ahead solar power generation forecasting of a 20 kW photovoltaic system, the developed forecasting is suitable for a reliable Microgrid energy management. In total four neural networks were proposed, namely: multi-layred perceptron, radial basis function, recurrent and a neural network ensemble consisting in ensembl...

The most common forecasting methods in business are based on exponential smoothing, and the most common time series in business are inherently non-negative. Therefore it is of interest to consider the properties of the potential stochastic models underlying exponential smoothing when applied to non-negative data. We explore exponential smoothing state space models for non-negative data under va...

To effectively predict cigarette sales and improve the competitiveness of tobacco business enterprises, the characteristics of actual cigarette sales were detailed analyzed. Due to the long-term growth trends, seasonal fluctuations and the nonlinearity of monthly sales, we established three single forecasting models, which are Exponential Smoothing (ES), Seasonal Decomposition (SD) and Radial B...