Innovative Second-Generation Wavelets Construction With Recurrent Neural Networks for Solar Radiation Forecasting

Solar radiation prediction is an important challenge for the electrical engineer because it is used to estimate the power developed by commercial photovoltaic modules. This paper deals with the problem of solar radiation prediction based on observed meteorological data. A 2-day forecast is obtained by using novel wavelet recurrent neural networks (WRNNs). In fact, these WRNNS are used to exploit the correlation between solar radiation and timescale-related variations of wind speed, humidity, and temperature. The input to the selected WRNN is provided by timescale-related bands of wavelet coefficients obtained from meteorological time series. The experimental setup available at the University of Catania, Italy, provided this information. The novelty of this approach is that the proposed WRNN performs the prediction in the wavelet domain and, in addition, also performs the inverse wavelet transform, giving the predicted signal as output. The obtained simulation results show a very low root-mean-square error compared to the results of the solar radiation prediction approaches obtained by hybrid neural networks reported in the recent literature

Estimating Potential GDP for the Romanian Economy. An Eclectic Approach

The paper provides potential output and output gap estimates for the Romanian economy in the period 1998-2008. Our approach consists in combining the production function structural method with several statistical de-trending methods. The contribution of our analysis to the scarce literature dealing with the estimation of the cyclical position of the Romanian economy is twofold. First, we identify the contribution of the production factors to the potential output growth. Second, we aggregate the results obtained through filtering techniques in a consensus estimate, ascribing to each method a weight inversely related to its revision stability. The results suggest for the period 2001-2008 an average annual growth rate of the potential output equal to 5.8%, but on a descending slope, due to the adverse developments in the macroeconomic context.potential GDP, output gap, NAIRU, business cycle

Application of Stationary Wavelet Support Vector Machines for the Prediction of Economic Recessions

This paper examines the efficiency of various approaches on the classification and prediction of economic expansion and recession periods in United Kingdom. Four approaches are applied. The first is discrete choice models using Logit and Probit regressions, while the second approach is a Markov Switching Regime (MSR) Model with Time-Varying Transition Probabilities. The third approach refers on Support Vector Machines (SVM), while the fourth approach proposed in this study is a Stationary Wavelet SVM modelling. The findings show that SW-SVM and MSR present the best forecasting performance, in the out-of sample period. In addition, the forecasts for period 2012-2015 are provided using all approaches

Filtering and Forecasting Spot Electricity Prices in the Increasingly Deregulated Australian Electricity Market

Modelling and forecasting the volatile spot pricing process for electricity presents a number of challenges. For increasingly deregulated electricity markets, like that in the Australian state of New South Wales, there is need to price a range of derivative securities used for hedging. Any derivative pricing model that hopes to capture the pricing dynamics within this market must be able to cope with the extreme volatility of the observed spot prices. By applying wavelet analysis, we examine both the price and demand series at different time locations and levels of resolution to reveal and differentiate what is signal and what is noise. Further, we cleanse the data of leakage from the high frequency, mean reverting price spikes into the more fundamental levels of frequency resolution. As it is from these levels that we base the reconstruction of our filtered series, we need to ensure they are least contaminated by noise. Using the filtered data, we explore time series models as possible candidates for explaining the pricing process and evaluate their forecasting ability. These models include one from the threshold autoregressive (AR) model. What we find is that models from the TAR class produce forecasts that best appear to capture the mean and variance components of the actual data.electricity; wavelets, time series models; forecasting

Univariate Potential Output Estimations for Hungary

Potential output figures are important ingredients of many macroeconomic models and are routinely applied by policy makers and global agencies. Despite its widespread use, estimation of potential output is at best uncertain and depends heavily on the model. The task of estimating potential output is an even more dubious exercise for countries experiencing huge structural changes, such as transition countries. In this paper we apply univariate methods to estimate and evaluate Hungarian potential output, paying special attention to structural breaks. In addition to statistical evaluation, we also assess the appropriateness of various methods by expertise judgement of the results, since we argue that mechanical adoption of univariate techniques might led to erroneous interpretation of the business cycle. As all methods have strengths and weaknesses, we derive a single measure of potential output by weighting those methods that pass both the statistical and expertise criteria. As standard errors, which might be used for deriving weights, are not available for some of the methods, we base our weights on similar but computable statistics, namely on revisions of the output gap for all dates by recursively estimating the models. Finally, we compare our estimated gaps with the result of the only published Hungarian output gap measure of Darvas-Simon (2000b), which is based on an economic model.combination, detrending, output gap, revision.

Univariate Potential Output Estimations for Hungary

Potential output figures are important ingredients of many macroeconomic modelsand are routinely applied by policy makers and global agencies. Despite itswidespread use, estimation of potential output is at best uncertain and dependsheavily on the model. The task of estimating potential output is an even moredubious exercise for countries experiencing huge structural changes, such astransition countries. In this paper we apply univariate methods to estimate andevaluate Hungarian potential output, paying special attention to structural breaks.In addition to statistical evaluation, we also assess the appropriateness of variousmethods by expertise judgement of the results, since we argue that mechanicaladoption of univariate techniques might led to erroneous interpretation of thebusiness cycle. As all methods have strengths and weaknesses, we derive a singlemeasure of potential output by weighting those methods that pass both thestatistical and expertise criteria. As standard errors, which might be used forderiving weights, are not available for some of the methods, we base our weightson similar but computable statistics, namely on revisions of the output gap for alldates by recursively estimating the models. Finally, we compare our estimated gapswith the result of the only published Hungarian output gap measure of Darvas-Simon (2000b), which is based on an economic model.ombination, detrending, new EU members, OCA, output gap, revision