In this paper we present an analysis of the results of a study into wholesale (spot) electricity price forecasting with Support Vector Machines (SVM) utilising past price and demand data and Projected Assessment of System Adequacy (PASA) data. The forecasting accuracy was evaluated using Australian National Electricity Market (NEM), New South Wales regional data over the year 2002. The inclusio...

Gold price forecasting has been a hot issue in economics recently. In this work, wavelet neural network (WNN) combined with a novel artificial bee colony (ABC) algorithm is proposed for this gold price forecasting issue. In this improved algorithm, the conventional roulette selection strategy is discarded. Besides, the convergence statuses in a previous cycle of iteration are fully utilized as ...

The Agricultural sector needs more support for its development in developing countries like India. Price prediction helps the farmers and also the Government to make effective decision. Based on the complexity of vegetable price prediction, making use of the classification technique like neural networks such as self build up the model of Back-propagation neural network (BPNN) to predict vegetab...

Purpose – Price forecasting is essential for risk management in deregulated electricity markets. The purpose of this paper is to propose a hybrid technique using wavelet transform (WT) and multiple linear regression (MLR) to forecast price profile in electricity markets. Design/methodology/approach – Price series is highly volatile and non-stationary in nature. In this work, initially complete ...

abstract the aim of this study was to selecting the suitable model for forecast land, production and price of sugar beet in iran. for this purpose, models applied to forecast are arima, single and double exponential smoothing, harmonic, artificial neural network and arch for period 1993-2008. results of durbin-watson tests, land, production and price of sugar beet series were found non stochast...

Improving return forecasting is very important for both investors and researchers in financial markets. In this study we try to aim this object by two new methods. First, instead of using traditional variable, gold prices have been used as predictor and compare the results with Goyal's variables. Second, unlike previous researches new machine learning algorithm called Deep learning (DP) has bee...

this paper investigates the forecasting gold coin futures contract price in iran mercantile exchange. this research has presented a hybrid model based on genetic fuzzy systems (gfs) and artificial neural network (ann) to forecast the gold futures contract, at first, we use stepwise regression analysis (sra) to determine factors which have most influence on stock prices. at the next stage we div...

This research brings forward the Pleione Formosana Hayata Orchid product market demand forecasting system to assist the traditional market personnel to forecast the demand of its customer in the future. The characteristic of Pleione Formosana orchid is one bulb with one leaf only. It sells by the bulbs. By the time for harvesting the operating personnel need spend much capital on the stock of b...

This paper examines the forward pricing efficiency of the local crude palm oil (CPO) futures market. In an efficient market, the relevant signal to be used by -the producers, traders and processors is simply the futures price. The forward pricing efficiency is measured in terms of the forecasting ability of Malaysian crude palm oil futures price on physical price. The relative predictive power ...