Abstract In the light of adverse effects climate change, data analysis and Machine Learning (ML) techniques can provide accurate forecasts, which enable efficient scheduling operation energy usage. Especially in built environment, Energy Load Forecasting (ELF) enables Distribution System Operators or Aggregators to accurately predict demand generation trade-offs. This paper focuses on developin...

The experience with deregulated electricity market has shown the increasingly important role of short-term electric load forecasting in the energy producing and scheduling. However, because of nonlinear, stochastic and nonstable characteristics associated with the electric load series, it is extremely difficult to precisely forecast the electric load. This paper aims to establish a novel ensemb...

A reliable multi-step predictor is very useful to a wide array of applications to forecast the behavior of dynamic systems. The objective of this paper is to develop a more robust data-driven predictor for time series forecasting. Based on simulation analysis, it is found that multi-step-ahead forecasting schemes based on step inputs perform better than those based on sequential inputs. It is a...

This paper proposes a hybrid multi-step-ahead forecasting model based on two stages to improve monthly pelagic fish-catch time-series modeling. In the first stage, the stationary wavelet transform is used to separate the raw time series into a high frequency (HF) component and a low frequency (LF) component, whereas the periodicities of each time series is obtained by using the Fourier power sp...

This paper presents a Gaussian process model-based short-term electric load forecasting. The Gaussian process model is a nonparametric model and the output of the model has Gaussian distribution with mean and variance. The multiple Gaussian process models as every hour ahead predictors are used to forecast future electric load demands up to 24 hours ahead in accordance with the direct forecasti...

The object of Bayesian modelling is the predictive distribution, which in a forecasting scenario enables evaluation of forecasted values and their uncertainties. In this paper we focus on reliably estimating the predictive mean and variance of forecasted values using Bayesian kernel based models such as the Gaussian Process and the Relevance Vector Machine. We derive novel analytic expressions ...

In finance, volatility is defined as a measure of variation of a trading price series over time. As volatility is a latent variable, several measures, named proxies, have been proposed in the literature to represent such quantity. The purpose of our work is twofold. On one hand, we aim to perform a statistical assessment of the relationships among the most used proxies in the volatility literat...