We discuss a variety of recent methodological advances that can be used to estimate mixed frequency factor-MIDAS models for the purpose of pastcasting, nowcasting, and forecasting. In order to illustrate the uses of this methodology, we introduce a new real-time Korean GDP dataset, and carry out a series of prediction experiments, using a two step approach. In a first step, we estimate common l...

Forecasting of solar irradiance will become a major issue in the future integration of solar energy resources into existing energy supply structures. As far as short-term time horizons (up to 2h) are concerned, satellite data are a high quality source for information about radiation with excellent temporal and spatial resolution. Due to the strong impact of cloudiness on surface irradiance the ...

Surface solar radiation forecasting permits to predict photovoltaic plant production for a massive and safe integration of solar energy into the electric network. For short-term forecasts (intra-day), methods using images from meteorological geostationary satellites are more suitable than numerical weather prediction models. Forecast schemes consist in assessing cloud motion vectors and in extr...

We propose and estimate fundamental models for natural gas prices. We compare how well these models, as well as univariate statistical time series models of NG prices and the NYMEX futures price for natural gas, forecast spot gas prices. We find that a univariate time series model that incorporates fundamental variables related to production, storage, weather, and aggregate output performs best...

Holes∗ S. W. Hawking DAMTP, University of Cambridge, UK Abstract It has been suggested [1] that the resolution of the information paradox for evaporating black holes is that the holes are surrounded by firewalls, bolts of outgoing radiation that would destroy any infalling observer. Such firewalls would break the CPT invariance of quantum gravity and seem to be ruled out on other grounds. A dif...

Abstract Engel and West (EW, 2005) argue that as the discount factor gets closer to one, presentvalue asset pricing models place greater weight on future fundamentals. Consequently, current fundamentals have very weak forecasting power and asset prices in these models appear to follow approximately a random walk. We connect the Engel-West explanation to the studies of long-horizon regressions. ...

Forecasting future values of time-series data is a critical task in many disciplines including financial planning and decision-making. Researchers practitioners statistics apply traditional statistical methods (such as ARMA, ARIMA, ES, GARCH) for long time with varying. accuracies. Deep learning provides more sophisticated non-linear approximation that supersede most cases. require minimal feat...

Long-term demand forecasting presents the first step in planning and developing future generation, transmission and distribution facilities. One of the primary tasks of an electric utility accurately predicts load demand requirements at all times, especially for long-term. Based on the outcome of such forecasts, utilities coordinate their resources to meet the forecasted demand using a least-co...

This paper considers the problem of forecasting in large macroeconomic panels using Bayesian model averaging. Theoretical justi...cations for averaging across models, as opposed to selecting a single model, are given. Practical methods for implementing Bayesian model averaging with factor models are described. These methods involve algorithms which simulate from the space de...ned by all possib...

The limited availability of local climatological stations and the limitations to predict wind speed (WS) accurately are significant barriers expansion energy (WE) projects worldwide. A methodology forecast WS at scale can be used overcome these barriers. This study proposes a with high-resolution long-term horizons, which combines Fourier model nonlinear autoregressive network (NAR). Given nonl...