- Editorial: Deploy effective fiscal initiatives and promote inclusive trade policies to escape from the low-growth trap - General assessment of the macroeconomic situation - Using the fiscal levers ...to escape the low-growth trap - Argentina - Australia - Austria - Belgium - Brazil - Canada - Chile - China - Colombia - Costa Rica - Czech Republic - Denmark - Estonia - Euro area - Finland - France - Germany - Greece - Hungary - Iceland - India - Indonesia - Ireland - Israel - Italy - Japan - Korea - Latvia - Lithuania - Luxembourg - Mexico - Netherlands - New Zealand - Norway - Poland - Portugal - Russia - Slovak Republic - Slovenia - South Africa - Spain - Sweden - Switzerland - Turkey - United Kingdom - United States - Statistical Annex.
...the International Monetary Fund Managing Director Kristalina Georgieva, forecasting a dramatic slow down in global economic growth due to the epidemic, announced the creation of $50 billion worth ...of funds to support low-income and emerging market countries in the response to COVID-19. ...there is even less funding for professional communications staffing at WHO, the various Centers for Disease Control and Prevention in Africa, Europe, North America, and Asia, or their counterpart offices nested in local departments of public health. Charly Triballeau/AFP/Getty Images If governments, agencies, and health organisations want people at risk of infection to respond to COVID-19 with an appropriate level of alert, to cooperate with health authorities, and to act with compassion and humanity, I believe that they must be willing to fund their messengers on an unprecedented scale, with genuine urgency. Getting ahead of COVID-19 requires not only slowing its spread, adequate funding for the health response, supporting research to advance our knowledge of it, integrated actions to mitigate the health, economic, and social impacts of the epidemic, among others, but also control of narratives regarding its scientific and clinical attributes and pandemic containment efforts—an effort that I do not think can be successful if executed on inadequate budgets by sleep-deprived communicators. Wall Street and the rest of the stock investment world are trying to calm markets, only to witness ongoing financial turmoil and huge stock market falls.
Understanding potential patterns in future population levels is crucial for anticipating and planning for changing age structures, resource and health-care needs, and environmental and economic ...landscapes. Future fertility patterns are a key input to estimation of future population size, but they are surrounded by substantial uncertainty and diverging methodologies of estimation and forecasting, leading to important differences in global population projections. Changing population size and age structure might have profound economic, social, and geopolitical impacts in many countries. In this study, we developed novel methods for forecasting mortality, fertility, migration, and population. We also assessed potential economic and geopolitical effects of future demographic shifts.
We modelled future population in reference and alternative scenarios as a function of fertility, migration, and mortality rates. We developed statistical models for completed cohort fertility at age 50 years (CCF50). Completed cohort fertility is much more stable over time than the period measure of the total fertility rate (TFR). We modelled CCF50 as a time-series random walk function of educational attainment and contraceptive met need. Age-specific fertility rates were modelled as a function of CCF50 and covariates. We modelled age-specific mortality to 2100 using underlying mortality, a risk factor scalar, and an autoregressive integrated moving average (ARIMA) model. Net migration was modelled as a function of the Socio-demographic Index, crude population growth rate, and deaths from war and natural disasters; and use of an ARIMA model. The model framework was used to develop a reference scenario and alternative scenarios based on the pace of change in educational attainment and contraceptive met need. We estimated the size of gross domestic product for each country and territory in the reference scenario. Forecast uncertainty intervals (UIs) incorporated uncertainty propagated from past data inputs, model estimation, and forecast data distributions.
The global TFR in the reference scenario was forecasted to be 1·66 (95% UI 1·33–2·08) in 2100. In the reference scenario, the global population was projected to peak in 2064 at 9·73 billion (8·84–10·9) people and decline to 8·79 billion (6·83–11·8) in 2100. The reference projections for the five largest countries in 2100 were India (1·09 billion 0·72–1·71, Nigeria (791 million 594–1056), China (732 million 456–1499), the USA (336 million 248–456), and Pakistan (248 million 151–427). Findings also suggest a shifting age structure in many parts of the world, with 2·37 billion (1·91–2·87) individuals older than 65 years and 1·70 billion (1·11–2·81) individuals younger than 20 years, forecasted globally in 2100. By 2050, 151 countries were forecasted to have a TFR lower than the replacement level (TFR <2·1), and 183 were forecasted to have a TFR lower than replacement by 2100. 23 countries in the reference scenario, including Japan, Thailand, and Spain, were forecasted to have population declines greater than 50% from 2017 to 2100; China's population was forecasted to decline by 48·0% (−6·1 to 68·4). China was forecasted to become the largest economy by 2035 but in the reference scenario, the USA was forecasted to once again become the largest economy in 2098. Our alternative scenarios suggest that meeting the Sustainable Development Goals targets for education and contraceptive met need would result in a global population of 6·29 billion (4·82–8·73) in 2100 and a population of 6·88 billion (5·27–9·51) when assuming 99th percentile rates of change in these drivers.
Our findings suggest that continued trends in female educational attainment and access to contraception will hasten declines in fertility and slow population growth. A sustained TFR lower than the replacement level in many countries, including China and India, would have economic, social, environmental, and geopolitical consequences. Policy options to adapt to continued low fertility, while sustaining and enhancing female reproductive health, will be crucial in the years to come.
Bill & Melinda Gates Foundation.
•A new hybrid method to integrate deep neural networks with multiple financial time series models is proposed.•Combines the LSTM model with various generalized autoregressive conditional ...heteroskedasticity (GARCH)-type models.•Compared performance of the proposed hybrid LSTM models with that of existing methodologies.
Volatility plays crucial roles in financial markets, such as in derivative pricing, portfolio risk management, and hedging strategies. Therefore, accurate prediction of volatility is critical. We propose a new hybrid long short-term memory (LSTM) model to forecast stock price volatility that combines the LSTM model with various generalized autoregressive conditional heteroscedasticity (GARCH)-type models. We use KOSPI 200 index data to discover proposed hybrid models that combine an LSTM with one to three GARCH-type models. In addition, we compare their performance with existing methodologies by analyzing single models, such as the GARCH, exponential GARCH, exponentially weighted moving average, a deep feedforward neural network (DFN), and the LSTM, as well as the hybrid DFN models combining a DFN with one GARCH-type model. Their performance is compared with that of the proposed hybrid LSTM models. We discover that GEW-LSTM, a proposed hybrid model combining the LSTM model with three GARCH-type models, has the lowest prediction errors in terms of mean absolute error (MAE), mean squared error (MSE), heteroscedasticity adjusted MAE (HMAE), and heteroscedasticity adjusted MSE (HMSE). The MAE of GEW-LSTM is 0.0107, which is 37.2% less than that of the E-DFN (0.017), the model combining EGARCH and DFN and the best model among those existing. In addition, the GEW-LSTM has 57.3%, 24.7%, and 48% smaller MSE, HMAE, and HMSE, respectively. The first contribution of this study is its hybrid LSTM model that combines excellent sequential pattern learning with improved prediction performance in stock market volatility. Second, our proposed model markedly enhances prediction performance of the existing literature by combining a neural network model with multiple econometric models rather than only a single econometric model. Finally, the proposed methodology can be extended to various fields as an integrated model combining time-series and neural network models as well as forecasting stock market volatility.
Non-ferrous metals are indispensable industrial materials and strategic supports of national economic development. The price forecasting of non-ferrous metals is critical for investors, policymakers, ...and researchers. Nevertheless, an accurate and robust non-ferrous metals price forecasting is a difficult yet challenging problem due to severe fluctuations and irregular cycles in the metal price evolution. Motivated by the ”Divide-and-Conquer” principle, we present a novel hybrid deep learning model, which combines the VMD (variational mode decomposition) method and the LSTM (long short-term memory) network to construct a forecasting model in this paper. Here, the VMD method is firstly employed to disassemble the original price series into several components. The LSTM network is used to forecast for each component. Lastly, the forecasting results of each component are aggregated to formulate an ultimate forecasting output for the original price series. To investigate the forecasting performance of the proposed model, extensive experiments have been executed using the LME (London Metal Exchange) daily future prices of Zinc, Copper and Aluminum, and other six state-of-the-art methods are included for comparison. The experiment results demonstrate that the proposed model has superior performance for non-ferrous metals price forecasting.
Ocean warming and acidification threaten the future growth of coral reefs. This is because the calcifying coral reef taxa that construct the calcium carbonate frameworks and cement the reef together ...are highly sensitive to ocean warming and acidification. However, the global-scale effects of ocean warming and acidification on rates of coral reef net carbonate production remain poorly constrained despite a wealth of studies assessing their effects on the calcification of individual organisms. Here, we present global estimates of projected future changes in coral reef net carbonate production under ocean warming and acidification. We apply a meta-analysis of responses of coral reef taxa calcification and bioerosion rates to predicted changes in coral cover driven by climate change to estimate the net carbonate production rates of 183 reefs worldwide by 2050 and 2100. We forecast mean global reef net carbonate production under representative concentration pathways (RCP) 2.6, 4.5, and 8.5 will decline by 76, 149, and 156%, respectively, by 2100. While 63% of reefs are projected to continue to accrete by 2100 under RCP2.6, 94% will be eroding by 2050 under RCP8.5, and no reefs will continue to accrete at rates matching projected sea level rise under RCP4.5 or 8.5 by 2100. Projected reduced coral cover due to bleaching events predominately drives these declines rather than the direct physiological impacts of ocean warming and acidification on calcification or bioerosion. Presently degraded reefs were also more sensitive in our analysis. These findings highlight the low likelihood that the world's coral reefs will maintain their functional roles without near-term stabilization of atmospheric CO
emissions.
Prediction of solar irradiance is essential for minimizing energy costs and providing high power quality in electrical power grids with distributed solar photovoltaic generations. However, for ...residential and small commercial users deploying on-site photovoltaic generations, the historical irradiance data can not be obtained directly because of expensive solar irradiance meters. Thanks to increasingly improved weather forecasting service provided by local meteorological organizations, weather forecasting data such as temperature, dew point, humidity, visibility, wind speed and descriptive weather summary, are becoming readily available through the Internet, while the irradiance forecasting data are often unavailable. This paper proposes a novel solar prediction scheme for hourly day-ahead solar irradiance prediction by using the weather forecasting data. This study formulates the prediction problem as a structured output prediction problem jointly predicting multiple outputs simultaneously. The proposed prediction model is trained by using long short-term memory (LSTM) networks taking into account the dependence between consecutive hours of the same day. We compare persistence algorithm, linear least square regression and multilayered feedforward neural networks using backpropagation algorithm (BPNN) for solar irradiance prediction. The experimental results on a dataset collected in island of Santiago, Cape Verde, demonstrate that the proposed algorithm outperforms these competitive algorithms for single output prediction. The proposed algorithm is %18.34 more accurate than BPNN in terms of root mean square error (RMSE) by using about 2 years training data to predict half-year testing data. Moreover, compared with BPNN, the proposed algorithm also shows less overfitting and better generalization capability. For a case using 10 years of historical data to predict 1 year of irradiance data, the prediction RMSE using the proposed LSTM algorithm decreases by 42.9% against BPNN.
•Weather forecasting data are used as input variables for irradiance prediction.•The prediction problem is formulated as a structured output prediction problem.•LSTM is applied to prediction of solar irradiance.
•Review on solar photovoltaic power forecasting techniques using time-series statistical, physical, and ensemble methods.•Classification of solar photovoltaic power forecasting ...techniques.•Comparative discussion of the solar photovoltaic power forecasting techniques with the respective resources.•Analysis of the metrics assessment is presented.
Solar photovoltaic plants are widely integrated into most countries worldwide. Due to the ever-growing utilization of solar photovoltaic plants, either via grid-connection or stand-alone networks, dramatic changes can be anticipated in both power system planning and operating stages. Solar photovoltaic integration requires the capability of handling the uncertainty and fluctuations of power output. In this case, solar photovoltaic power forecasting is a crucial aspect to ensure optimum planning and modelling of the solar photovoltaic plants. Accurate forecasting provides the grid operators and power system designers with significant information to design an optimal solar photovoltaic plant as well as managing the power of demand and supply. This paper presents an extensive review on recent advancements in the field of solar photovoltaic power forecasting. This paper aims to analyze and compare various methods of solar photovoltaic power forecasting in terms of characteristics and performance. This work classifies solar photovoltaic power forecasting methods into three major categories i.e., time-series statistical methods, physical methods, and ensemble methods. To date, Artificial Intelligence approaches are widely used due to their capability in solving the non-linear and complex structure of data. The performance analysis shows that these methods outperform the traditional methods. Recently, the ensemble methods were also developed by researchers to extract the unique features of single models to enhance the forecast model performances. This combination produces accurate results compared to individual models. This paper also elaborates on the metrics assessment which was implemented to evaluate the forecast model performances. This work provides information which is beneficial for researchers and engineers who are involved in the modelling and planning of the solar photovoltaic plant.
For optimal power system operation, electrical generation must follow electrical load demand. So, short term load forecast (STLF) has been proposed by researchers to tackle the mentioned problem. Not ...merely has it been researched extensively and intensively, but also a variety of forecasting methods has been raised. This paper outlines a new prediction model for small scale load prediction i.e., buildings or sites. The proposed model is based on improved version of empirical mode decomposition (EMD) which is called sliding window EMD (SWEMD), a new feature selection algorithm and hybrid forecast engine. The aims of proposed feature selection algorithm is to maximize the relevancy and minimize the redundancy criterion based on Pearson's correlation (MRMRPC) coefficient. Finally, an improved Elman neural network (IENN) based forecast engine proposed to predict the load signal in this procedure. All weights of this forecast engine have been optimized with an intelligent algorithm to find better prediction results. Effectiveness of the proposed model is carried out to real-world engineering test case in comparison with other prediction models.
•Presenting a new forecast model in smart grid.•Presenting an improved stochastic algorithm for optimization.•Application of sliding window EMD in prediction process.•Implementation of new feature selection to the mentioned problem.•Application of new hybrid forecast engine based on improved ENN.
Power demand forecasting with high accuracy is a guarantee to keep the balance between power supply and demand. Due to strong volatility of industrial power load, ultra-short-term power demand is ...difficult to forecast accurately and robustly. To solve this problem, this article proposes a Long Short-Term Memory (LSTM) network based hybrid ensemble learning forecasting model. A hybrid ensemble strategy-which consists of Bagging, Random Subspace, and Boosting with ensemble pruning-is designed to extract the deep features from multivariate data, and a new loss function that integrates peak demand forecasting error is proposed according to bias-variance tradeoff. Experimental results on open dataset and practical dataset show that the proposed model outperforms several state-of-the-art time series forecasting models, and obtains higher accuracy and robustness to forecast peak demand.
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