6 Highly accurate interval forecasting of a stock price index is fundamental to 7 successfully making a profit when making investment decisions, by providing a range 8 of values rather than a point estimate. In this study, we investigate the possibility of 9 forecasting an interval-valued stock price index series over short and long horizons 10 using multi-output support vector regression (MSVR...

A major problem for many organisational forecasters is to choose the appropriate forecasting method for a large number of time series. Various selection rules have been proposed in order to enhance forecasting accuracy. The simpler approach for model selection involves the identification of a single method, which is applied to all data series in an aggregate manner, without taking into account ...

Abstract Long-term forecasting involves predicting a horizon that is far ahead of the last observation. It problem high practical relevance, for instance companies in order to decide upon expensive long-term investments. Despite recent progress and success Gaussian processes (GPs) based on spectral mixture kernels, remains challenging these kernels because they decay exponentially at large hori...

We investigate whether a class of trend models, which decompose time series into an underlying and transitory component, with various error term structures can improve upon the forecast performance commonly used models when forecasting consumer price index (CPI) inflation in Australia. The main result is that tend to provide more accurate point density forecasts at medium long horizons compared...

Short-term wind and wind power forecasts are required for the reliable and economic operation of power systems with significant wind power penetration. This thesis presents new statistical techniques for producing forecasts at multiple locations using spatiotemporal information. Forecast horizons of up to 6 hours are considered for which statistical methods outperform physical models in general...

Accurate models for electric power load forecasting are essential to the operation and planning of a power utility company. Load forecasting helps electric utility to make important decisions on trading of power, load switching, and infrastructure development. Load forecasts are extremely important for power utilizes ISOs, financial institutions, and other stakeholder of power sector. Short ter...

Electricity consumption forecasting plays an important role in investment planning of electricity infrastructure, and production/generation distribution. Accurate prediction over the mid/long term is great interest to both practitioners academics. Considering that monthly series usually show obvious seasonal variation due their inherent nature subject temperature during year, this paper, expone...

Load forecasting (LF) is an essential factor in power system management. LF helps the utility maximize utilization of power-generating plants and schedule them both reliably economically. In this paper, a novel hybrid method proposed, combining long short-term memory network (LSTM) neural prophet (NP) through artificial network. The paper aims to predict electric load for different time horizon...

The roll of consumption energy forecasting is very important to make planning time-horizon strategy, and mitigate a great management. As result, improving the sustainability energy, creating clean environment. Aiming develop in different time horizons, this work gives results new hybrid method, which combine deep echo state network (DeepESN), with Binary genetic algorithm (BGA). DeepESN an exte...

This paper presents a novel hybrid method for short-term load forecasting. The system comprises of two artificial neural networks (ANN), assembled in a hierarchical order. The first ANN is a multilayer perceptron (MLP) which functions as integrated load predictor (ILP) for the forecasting day. The output of the ILP is then fed to another, more complex MLP, which acts as an hourly load predictor...