Forecasting future values of time-series data is a critical task in many disciplines including financial planning and decision-making. Researchers practitioners statistics apply traditional statistical methods (such as ARMA, ARIMA, ES, GARCH) for long time with varying. accuracies. Deep learning provides more sophisticated non-linear approximation that supersede most cases. require minimal feat...

The purpose of this paper is to forecast monthly gasoline prices in Jordan by applying Gaussian process regression on two types (octane-90 and octane-95) during the period January 2008 December 2019. Accurately predicting have several fiscal policy implications concerning fuel subsidies taxes. Also, they affect consumption production decisions. Moreover, are crucial for designing analyzing envi...

The past few years have witnessed a growing rate of attraction in adoption of Artificial Intelligence (AI) techniques and combining them to improve forecasting accuracy in different fields. Besides, stock market forecasting has always been a subject of interest for most investors and professional analysts. Stock market forecasting is a tough problem because of the uncertainties involved in the ...

Crude oil prices do play significant role in the global economy and are a key input into option pricing formulas, portfolio allocation, and risk measurement. In this paper, a hybrid model integrating wavelet and multiple linear regressions (MLR) is proposed for crude oil price forecasting. In this model, Mallat wavelet transform is first selected to decompose an original time series into severa...

We propose a new time series model aimed at forecasting crude oil prices. The proposed specification is an unobserved components model with an asymmetric cyclical component. The asymmetric cycle is defined as a sine-cosine wave where the frequency of the cycle depends on past oil price observations. We show that oil price forecasts improve significantly when this asymmetry is explicitly modelled.

Airline seat inventory control is a very profitable tool in the airline industry. Mathematical programming models provide booking limits or bid-prices for all itineraries and fare classes based on demand forecasts. But the actual revenue generated in the booking process fails to meet expectations. Simple deterministic models based on expected demand generate better revenue than more advanced pr...

This paper develops an efficiency-wage model where input prices affect the equilibrium rate of unemployment. We show that a simple framework based on only two prices (the real price of oil and the real rate of interest) is able to explain the main post-war movements in the rate of U.S. joblessness. The equations do well in forecasting unemployment many years out-of-sample, and provide evidence ...

Forecasting the short-term trend of a stock market has long been a big challenging task. Parameters of stock markets, including open/close prices, daily-high/low prices and trading volumes, were frequently used in previous studies to forecast the stock market. Basing on the fact that the moving direction of these parameters have certain inertia within short-term period, we here explored the pot...