Internet search activity data has been widely used as an instrument to approximate trader attention in different markets. This method has proven effective in predicting market indices in the short-term. However, little attention has been paid to comparing various search keywords and finding the...

The present work attempts to evaluate the advantages inherent to the use of exogenous variables highly correlated to the electric load, for the forecast of future demand. Here we utilize time series models of the auto-regressive moving average types incorporating seasonal treatment and exogenous...

Recently it has become clear that many technologies follow a generalized version of Moore's law, i.e. costs tend to drop exponentially, at different rates that depend on the technology. Here we formulate Moore's law as a correlated geometric random walk with drift, and apply it to historical...

This research uses annual time series data on CO2 emissions in India from 1960 to 2017, to model and forecast CO2 using the Box – Jenkins ARIMA approach. Our diagnostic tests indicate that India CO2 emission data is I (2). The study presents the ARIMA (2, 2, 0) model. The diagnostic tests...

This study evaluates oil price forecasts based on their economic significance for macroeconomic predictions. More specifically, we first use the current state-of-the-art frameworks to forecast monthly oil prices and subsequently we use these forecasts, as oil price assumptions, to predict...

With the on-going expansion of renewable energy generation, short-term trading, notably in intraday markets, becomes increasingly relevant to cope with forecast updates for renewable infeed's. In this context, we develop a multivariate model of wind forecasting trajectories in order to support...

In recent years, the international community has been increasing its efforts to reduce the human footprint on air pollution and global warming. Total CO2 emissions are a key component of global emissions, and as such, they are closely monitored by national and supranational entities. This study...

We study the out-of-sample predictability of the real price of crude oil using forecast combinations constructed from several individual predictors. We find that forecasts of themonthly average price of oil are more accurate than the no-change forecast at horizons ranging from 1 to 24 months...

In this study, we present an empirical comparison of statistical models and machine learning models for daily electricity price forecasting in the New Zealand electricity market. We demonstrate the effectiveness of GARCH and SV models and their t-distribution variants when paired with feature...