Our paper suggests a simple, recursive residuals (out-of-sample) graphical approach to evaluating the predictive power of popular equity premium and stock market timeseries forecasting regressions. When applied, we find that dividend ratios should have been known to have no predictive ability even prior to the 1990s, and that any seeming ability even then was driven by only two years, 1973 and ...

Accurate and effective electricity price forecasting is critical to market participants in order to make an appropriate risk management in competitive electricity markets. Market participants rely on price forecasts to decide on their bidding strategies, allocate assets and plan facility investments. However, due to its time variant behavior and non-linear and non-stationary nature, electricity...

This paper introduces a complement statistical test for distinguishing between the predictive accuracy of two sets of forecasts. We propose a non-parametric test founded upon the principles of the Kolmogorov-Smirnov (KS) test, referred to as the KS Predictive Accuracy (KSPA) test. The KSPA test is able to serve two distinct purposes. Initially, the test seeks to determine whether there exists a...

accurate and effective electricity price forecasting is critical to market participants in order to make an appropriate risk management in competitive electricity markets. market participants rely on price forecasts to decide on their bidding strategies, allocate assets and plan facility investments. however, due to its time variant behavior and non-linear and non-stationary nature, electricity...

Abstract- In a typical competitive electricity market, a large number of short-term and long-term contracts are set on basis of energy price by an Independent System Operator (ISO). Under such circumstances, accurate electricity price forecasting can play a significant role in improving the more reasonable bidding strategies adopted by the electricity market participants. So, they cannot only r...

Accurate indoor temperature forecasting can facilitate energy savings of the building without compromising occupant comfort level, by providing more accurate control HVAC (heating, ventilating, and air conditioning) system. In order to make best use different input variables, a long short-term memory (LSTM) based sequence (seq2seq) model was proposed multi-step ahead forecasting. The out-of-sam...

The detailed prediction of the upcoming wind on farms can support optimization energy production and operation maintenance. Numerical Weather Prediction (NWP) tools allow to simulate over long-term forecasting horizons (up several days) with a spatial resolution ranging between continental level down few hundred meters. We present methodology, based upon Computational Fluid Dynamics (CFD) Reyno...

Time series forecasting has gained much attention due to its many practical applications. Higher-order neural network with recurrent feedback is a powerful technique that has been used successfully for time series forecasting. It maintains fast learning and the ability to learn the dynamics of the time series over time. Network output feedback is the most common recurrent feedback for many recu...

Balancing the production and consumption of electricity is an urgent task. Its implementation largely depends on means methods planning production. Forecasting one tools since availability accurate forecast a mechanism for increasing validity management decisions. This study provides overview used to predict supply requirements different objects. The have been reviewed analytically, taking into...