Abstract
The traditional parking generation rate model can no longer forecast the demand of parking slots accurately under the pattern of shared parking. Shared parking, which can make full use of ...the free time of private or non-private parking slots, has become an effective way to ease the pressure of urban parking. Therefore, shared parking behavior selection generation (SPBSG) model is established, based on the analysis of residents’ shared parking selection behavior. The SPBSG model fully simulates residents’ parking choice preferences, shared slots management, parking time differences between different land types, and walking distance after parking. Experiment shows that the SPBSG model can reduce parking slots by 24.45% compared with the traditional parking demand prediction method.
Despite the importance of diabetes for global health, the future economic consequences of the disease remain opaque. We forecast the full global costs of diabetes in adults through the year 2030 and ...predict the economic consequences of diabetes if global targets under the Sustainable Development Goals (SDG) and World Health Organization Global Action Plan for the Prevention and Control of Noncommunicable Diseases 2013-2020 are met.
We modeled the absolute and gross domestic product (GDP)-relative economic burden of diabetes in individuals aged 20-79 years using epidemiological and demographic data, as well as recent GDP forecasts for 180 countries. We assumed three scenarios: prevalence and mortality
) increased only with urbanization and population aging (baseline scenario),
) increased in line with previous trends (past trends scenario), and
) achieved global targets (target scenario).
The absolute global economic burden will increase from U.S. $1.3 trillion (95% CI 1.3-1.4) in 2015 to $2.2 trillion (2.2-2.3) in the baseline, $2.5 trillion (2.4-2.6) in the past trends, and $2.1 trillion (2.1-2.2) in the target scenarios by 2030. This translates to an increase in costs as a share of global GDP from 1.8% (1.7-1.9) in 2015 to a maximum of 2.2% (2.1-2.2).
The global costs of diabetes and its consequences are large and will substantially increase by 2030. Even if countries meet international targets, the global economic burden will not decrease. Policy makers need to take urgent action to prepare health and social security systems to mitigate the effects of diabetes.
Welch and Goyal (2008) find that numerous economic variables with in-sample predictive ability for the equity premium fail to deliver consistent out-of-sample forecasting gains relative to the ...historical average. Arguing that model uncertainty and instability seriously impair the forecasting ability of individual predictive regression models, we recommend combining individual forecasts. Combining delivers statistically and economically significant out-of-sample gains relative to the historical average consistently over time. We provide two empirical explanations for the benefits of forecast combination: (i) combining forecasts incorporates information from numerous economic variables while substantially reducing forecast volatility; (ii) combination forecasts are linked to the real economy.
The Model Confidence Set Hansen, Peter R.; Lunde, Asger; Nason, James M.
Econometrica,
March 2011, Volume:
79, Issue:
2
Journal Article
Peer reviewed
This paper introduces the model confidence set (MCS) and applies it to the selection of models. A MCS is a set of models that is constructed such that it will contain the best model with a given ...level of confidence. The MCS is in this sense analogous to a confidence interval for a parameter. The MCS acknowledges the limitations of the data, such that uninformative data yield a MCS with many models, whereas informative data yield a MCS with only a few models. The MCS procedure does not assume that a particular model is the true model; in fact, the MCS procedure can be used to compare more general objects, beyond the comparison of models. We apply the MCS procedure to two empirical problems. First, we revisit the inflation forecasting problem posed by Stock and Watson (1999), and compute the MCS for their set of inflation forecasts. Second, we compare a number of Taylor rule regressions and determine the MCS of the best regression in terms of in-sample likelihood criteria.
Precipitation nowcasting, the high-resolution forecasting of precipitation up to two hours ahead, supports the real-world socioeconomic needs of many sectors reliant on weather-dependent ...decision-making
. State-of-the-art operational nowcasting methods typically advect precipitation fields with radar-based wind estimates, and struggle to capture important non-linear events such as convective initiations
. Recently introduced deep learning methods use radar to directly predict future rain rates, free of physical constraints
. While they accurately predict low-intensity rainfall, their operational utility is limited because their lack of constraints produces blurry nowcasts at longer lead times, yielding poor performance on rarer medium-to-heavy rain events. Here we present a deep generative model for the probabilistic nowcasting of precipitation from radar that addresses these challenges. Using statistical, economic and cognitive measures, we show that our method provides improved forecast quality, forecast consistency and forecast value. Our model produces realistic and spatiotemporally consistent predictions over regions up to 1,536 km × 1,280 km and with lead times from 5-90 min ahead. Using a systematic evaluation by more than 50 expert meteorologists, we show that our generative model ranked first for its accuracy and usefulness in 89% of cases against two competitive methods. When verified quantitatively, these nowcasts are skillful without resorting to blurring. We show that generative nowcasting can provide probabilistic predictions that improve forecast value and support operational utility, and at resolutions and lead times where alternative methods struggle.
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