This paper proposes a new approach to hybrid forecasting methodology, characterized as the statistical recalibration of forecasts from fundamental market price formation models. Such hybrid methods based upon fundamentals are particularly appropriate to medium term forecasting and in this paper the application is to month-ahead, hourly prediction of electricity wholesale prices in Spain. The re...

Support Vector Regression (SVR) has been widely applied in time series forecasting. Considering long term predictions, iterative predictions perform many one-step-ahead predictions until the desired horizon is achieved. This process accumulates the error from previous predictions and may affect the quality of forecasts. In order to improve long term iterative predictions a hybrid multiple Autor...

It is important for executives to predict the future trends. Otherwise, their companies cannot make profitable decisions and investments. The Bass diffusion model can describe the empirical adoption curve for new products and technological innovations. The Grey model provides shortterm forecasts using four data points. This study develops a combined model based on the rolling Grey model RGM and...

Nowadays, forecasting and techniques used to obtain the forecasts are very important. The term of forecast means to make an inference (predict) about the future on the basis of existing information. Especially, forecasting of stock market data are frequently used in time series analysis literature. Moreover, fuzzy time series forecasting methods have been widely used in the analysis of stock ma...

In this paper we propose a new engine management system for hybrid vehicles to enable energy providers and car manufacturers to provide new services. Energy forecasts are used to collaboratively orchestrate the behaviour of engine management systems of a fleet of PHEV’s to absorb oncoming energy in an smart manner. Cooperative algorithms are suggested to manage the energy absorption in an optim...

The article describes the method of forecasting time series of cash withdrawals in ATMs. The hybrid model is based on two methods: Holt-Winters additive method and Markov chainbased model. The combination occurs with the help of weight coefficients which are calculated on the basis of work of each model. Holt-Winters method forecasts time series with trend and seasonal variations. Markov chain ...

This paper presents a method for prediction short-term power demand of a vehicular power system. The forecasting of power demand is presented using wavelet decomposition and artificial neural network, a hybrid model which absorbs some merits of wavelet transform and neural network. The power demand time series is first decomposed into a certain number of levels with discreet wavelet transform a...

According to a number of forecasts, the microelectronics industry is at the threshold of revolutionary changes. One of the anticipated changes is the future transition to a high-volume production of hybrid chip systems. In this paper we consider issues associated with the manufacturing organization of such systems. A simple cost-volume model developed in this work identi es economic conditions ...

This paper reviews non-probabilistic approaches of modelling uncertainty, particularly in flood forecasting and introduces a fuzzy set theory-based method for treating precipitation uncertainty in rainfall-runoff modelling, which allows the temporal and/or spatial disaggregation of precipitation. The results of the fuzzy set theory-based method are compared with the probabilistic approach using...