Electricity prices strongly depend on seasonality of different time scales, therefore any forecasting electricity has to account for it. Neural networks have proven successful in short-term price-forecasting, but complicated architectures like LSTM are used integrate the seasonal behavior. This paper shows that simple neural network DNNs with an embedding layer information can generate a compet...

This paper establishes a new price-sensitive demand model which considers stochastic disturbance similar to ARMA(1,1) model. We examine the impact of two forecasting methods on the bullwhip effect in a two-stage supply chain with two retailers. It is assumed that two retailers face the same demand model and an order-up-to inventory policy is employed. The lead-time demand is forecasted respecti...

        This paper proposes a new forecasting model for investigating relationship between the price of crude oil, as an important energy source and GDP of the US, as the largest oil consumer, and the UK, as the oil producer. GMDH neural network and MLFF neural network approaches, which are both non-linear models, are employed to forecast GDP responses to the oil price changes. The resul...

in general, energy prices, such as those of crude oil, are affected by deterministic events such as seasonal changes as well as non-deterministic events such as geopolitical events. it is the non-deterministic events which cause the prices to vary randomly and makes price prediction a difficult task. one could argue that these random changes act like noise which effects the deterministic variat...

The time evolution of aggregate economic variables, such as stock prices, is affected by market expectations of individual investors. Neo-classical economic theory assumes that individuals form expectations rationally, thus enforcing prices to track economic fundamentals and leading to an efficient allocation of resources. However, laboratory experiments with human subjects have shown that indi...

the difficulty of determining intrinsic value of stock prices have led many people to use technical analysis in order to forecast stock prices in the future. to predict the stock price we need to determine the generating process of stock prices. in recent years many time - series methods have been used for forecasting purposes. one of these methods is the rescaled range analysis (ris). in the a...

This paper explores the implications of asset return predictability on longterm portfolio choice when return forecasting variables exhibit long memory. We model long memory using the class of fractionally integrated time series models. Important predictor variables for U.S. data, like the dividend-price ratio and nominal and real interest rates, are non-stationary with orders of integration aro...

In autumn 2021, the world faced first round of energy crisis [...]