Electricity price prediction through statistical and machine learning techniques captures market trends would be a useful tool for energy traders to observe fluctuations increase their profits over time. A Nonlinear AutoRegressive Moving Average model with eXogenous inputs (NARMAX) identifies key energy-related factors that influence hourly electricity modelling. We propose use transparent NARM...

Housing market dynamics have primarily shifted from consumption- to investment-driven in many countries, including Australia. Building on investment theory, we investigated by placing demand at the center using error correction model (ECM). We found that house prices, rents, and interest rates are cointegrated long run under present value framework. Other economic factors such as population gro...

Crude oil is the mixture of petroleum liquids and gases that extracted from ground by wells. It an important source fuel used in production several products. Given role price crude plays, it becomes extremely for managers to predict future while making operational decisions such as: when purchase material, how much produce what modes transportation use. The goal this paper develop a forecasting...

The main methodologies used in electricity price forecasting have been reviewed in this paper. The following price-forecasting techniques have been covered: (i) stochastic time series, (ii) causal models, and (iii) artificial intelligence based models. The quantitative analysis of the work done by various authors has been presented based on (a) time horizon for prediction, (b) input variables, ...

Many risk management strategies, including hedging the price risk using forward or futures contracts require accurate forecasts of basis, i.e., spot price minus the futures price. Recent literature in this area has applied nonlinear time-series models, which are refinements of the linear autoregressive models that allow the parameters to transition from one regime to another. These parametric n...

The worldwide electric power industry has seen many changes over the last 20 years. During this period many regulated or state-owned monopoly markets have been deregulated. In an electricity market, electricity price is decided based on demand and supply bids from the market participants; therefore, the importance of ShortTerm Load Forecasting (STLF) has been rising in these markets [1]. Load f...

Stock market is affected by news and information. If the stock market is not efficient, the reaction of stock price to news and information will place the stock market in overreaction and under-reaction states. Many models have been already presented by using different tools and techniques to forecast the stock market behavior. In this study, the reaction of stock price in the stock market was ...

electricity price predictions have become a major discussion on competitive market under deregulated power system. but, the exclusive characteristics of electricity price such as non-linearity, non-stationary and time-varying volatility structure present several challenges for this task. in this paper, a new forecast strategy based on the iterative neural network is proposed for day-ahead price...