Forecasting Death Rates Using Exogenous Determinants

Mortality models used for forecasting are predominantly based on the statistical properties of time series and do not generally incorporate an understanding of the forces driving secular trends. This paper addresses three research questions: Can the factors found in stochastic mortality-forecasting models be associated with real-world trends in health-related variables? Does inclusion of health-related factors in models improve forecasts? Do resulting models give better forecasts than existing stochastic mortality models? We consider whether the space spanned by the latent factor structure in mortality data can be adequately described by developments in gross domestic product, health expenditure and lifestyle-related risk factors using statistical techniques developed in macroeconomics and finance. These covariates are then shown to improve forecasts when incorporated into a Bayesian hierarchical model. Results are comparable or better than benchmark stochastic mortality models.

Bandwidth selection by cross-validation for forecasting long memory financial time series

The paper addresses the issue of choice of bandwidth in the application of semiparametric estimation of the long memory parameter in a univariate time series process. The focus is on the properties of forecasts from the long memory model. A variety of cross-validation methods based on out of sample forecasting properties are proposed. These procedures are used for the choice of bandwidth and subsequent model selection. Simulation evidence is presented that demonstrates the advantage of the proposed new methodology.

Is China's fifth-largest inland lake to dry-up? Incorporated hydrological and satellite-based methods for forecasting Hulun Lake water levels

Hulun Lake, China's fifth-largest inland lake, experienced severe declines in water level in the period of 2000-2010. This has prompted concerns whether the lake is drying up gradually. A multi-million US dollar engineering project to construct a water channel to transfer part of the river flow from a nearby river to maintain the water level was completed in August 2010. This study aimed to advance the understanding of the key processes controlling the lake water level variation over the last five decades, as well as investigate the impact of the river transfer engineering project on the water level. A water balance model was developed to investigate the lake water level variations over the last five decades, using hydrological and climatic data as well as satellite-based measurements and results from land surface modelling. The investigation reveals that the severe reduction of river discharge (-364±64 mm/yr, ∼70% of the five-decade average) into the lake was the key factor behind the decline of the lake water level between 2000 and 2010. The decline of river discharge was due to the reduction of total runoff from the lake watershed. This was a result of the reduction of soil moisture due to the decrease of precipitation (-49±45 mm/yr) over this period. The water budget calculation suggests that the groundwater component from the surrounding lake area as well as surface run off from the un-gauged area surrounding the lake contributed ∼ net 210 Mm3/yr (equivalent to ∼ 100 mm/yr) water inflows into the lake. The results also show that the water diversion project did prevent a further water level decline of over 0.5 m by the end of 2012. Overall, the monthly water balance model gave an excellent prediction of the lake water level fluctuation over the last five decades and can be a useful tool to manage lake water resources in the future.

Hybrid probabilistic wind power forecasting using temporally local Gaussian process

The demand for sustainable development has resulted in a rapid growth in wind power worldwide. Despite various approaches have been proposed to improve the accuracy and to overcome the uncertainties associated with traditional methods, the stochastic and variable nature of wind still remains the most challenging issue in accurately forecasting wind power. This paper presents a hybrid deterministic-probabilistic method where a temporally local ‘moving window’ technique is used in Gaussian Process to examine estimated forecasting errors. This temporally local Gaussian Process employs less measurement data while faster and better predicts wind power at two wind farms, one in the USA and the other in Ireland. Statistical analysis on the results shows that the method can substantially reduce the forecasting error while more likely generate Gaussian-distributed residuals, particularly for short-term forecast horizons due to its capability to handle the time-varying characteristics of wind power.

A methodology to analyse the impact of offshore wind forecasting error on electricity markets

Currently wind power is dominated by onshore wind farms in the British Isles, but both the United Kingdom and the Republic of Ireland have high renewable energy targets, expected to come mostly from wind power. However, as the demand for wind power grows to ensure security of energy supply, as a potentially cheaper alternative to fossil fuels and to meet greenhouse gas emissions reduction targets offshore wind power will grow rapidly as the availability of suitable onshore sites decrease. However, wind is variable and stochastic by nature and thus difficult to schedule. In order to plan for these uncertainties market operators use wind forecasting tools, reserve plant and ancillary service agreements. Onshore wind power forecasting techniques have improved dramatically and continue to advance, but offshore wind power forecasting is more difficult due to limited datasets and knowledge. So as the amount of offshore wind power increases in the British Isles robust forecasting and planning techniques are even more critical. This paper presents a methodology to investigate the impacts of better offshore wind forecasting on the operation and management of the single wholesale electricity market in the Republic of Ireland and Northern Ireland using PLEXOS for Power Systems. © 2013 IEEE.

Special condition wind power forecasting based on Gaussian process and similar historical data

Due to the variability of wind power, it is imperative to accurately and timely forecast the wind generation to enhance the flexibility and reliability of the operation and control of real-time power. Special events such as ramps, spikes are hard to predict with traditional methods using solely recently measured data. In this paper, a new Gaussian Process model with hybrid training data taken from both the local time and historic dataset is proposed and applied to make short-term predictions from 10 minutes to one hour ahead. A key idea is that the similar pattern data in history are properly selected and embedded in Gaussian Process model to make predictions. The results of the proposed algorithms are compared to those of standard Gaussian Process model and the persistence model. It is shown that the proposed method not only reduces magnitude error but also phase error.

Electric vehicle capacity forecasting model with application to load levelling

There are many uncertainties in forecasting the charging and discharging capacity required by electric vehicles (EVs) often as a consequence of stochastic usage and intermittent travel. In terms of large-scale EV integration in future power networks this paper develops a capacity forecasting model which considers eight particular uncertainties in three categories. Using the model, a typical application of EVs to load levelling is presented and exemplified using a UK 2020 case study. The results presented in this paper demonstrate that the proposed model is accurate for charge and discharge prediction and a feasible basis for steady-state analysis required for large-scale EV integration.

Forecasting invasions: resource use by mussels informs invasion patterns along the South African coast

Invasive species are often more able to rapidly and efficiently utilise resources than natives, and comparing per capita resource use at different resource densities among invaders and trophically analogous natives could allow for reliable predictions of invasiveness. In South Africa, invasion by the Mediterranean mussel Mytilus galloprovincialis has transformed wave-exposed shores, negatively affecting native mussel species. Currently, South Africa is experiencing a second mussel invasion with the recent detection of the South American Semimytilus algosus. We tested per capita uptake of an algal resource by invading M. galloprovincialis, S. algosus, and the native Aulacomya atra at different algal concentrations and temperatures, representing the west and south coasts of South Africa, to examine whether their per capita resource use could be a predictor of their spread and subsequent invasiveness. Regardless of temperature, M. galloprovincialis was the most efficient consumer, significantly reducing algal cells compared to the other species when the resource was presented in both low and high starting densities. Furthermore, these findings aligned with a greater biomass of M. galloprovincialis on the shore in comparison with the other species. Resource use by the new invader S. algosus was dependent on the density of resource and, although this species was efficient at low algal concentrations at cooler temperatures, this pattern broke down at higher algal densities. This was once more reflected in lower biomass in surveys of this species along the cool west coast. We therefore forecast that S. algosus will be become established along the south coast; however, we also predict that M. galloprovincialis will maintain dominance on these shores.

Exotic Endurance: Tourism, Fitness and the Marathon des Sables

This paper critically examines the intersections of global tourism and fitness in the Marathon des Sables, an annual ultramarathon in the Sahara desert in which over a thousand athletes run the equivalent of five marathons in six days. It demonstrates how the globalization of health and fitness resonates with familiar Western productions of exotic cultures for the purposes of tourist consumption. Of particular interest here is how established colonial asymmetries are recast in a neoliberal context as runners test their resilience, endurance and strength against an ‘extreme’ Saharan landscape. While the paper calls attention to these asymmetries, it is more concerned with troubling reductive colonial encounters in order to reveal their instability, heterogeneity and ambivalence. Indeed, the central conceit of the Marathon des Sables – that superior Western fitness regimes and technologies will dominate the race – is inverted by the overwhelming success of Moroccan runners and disaggregated by the biopolitical regulation of elite running bodies. These unexpected intersections of global tourism and fitness demand further attention because they reconfigure our received notions of who (and what) is capable of exerting agency in postcolonial encounters.

Off-Duty Resilience: Re-orienting Tourism, Leisure and Recreation in the US Army BOSS program

Understanding how US imperial strategy is sustained by tourism and militarism requires an account of how American soldiers learn to understand themselves in relation to a variety of marginalized others. This paper explores how the US Army’s ‘Ready and Resilient’ (R2) campaign constructs soldier / other relations by mobilizing off-duty time through the ‘Better Opportunities for Single Soldiers’ (BOSS) program. BOSS’s first two platforms of ‘Well-Being’ and ‘Community Service’ feed into the R2 agenda by producing highly-skilled leaders (who govern a disengaged rank and file) and benevolent humanitarians (who provide charity for abject civilians). When these dispositions are transposed into BOSS’s third platform of ‘Recreation and Leisure’, soldiers turn away from the goals of leadership and humanitarianism to reveal the privileged narcissism underscoring the R2 agenda. This self-focus is intensified by familiar power relations in the tourism industry as soldiers pursue self-improvement by commodifying, distancing and effacing local tourist workers. Using the BOSS program as a case study, this paper critically interrogates how the US Army is assimilating off-duty practices of tourism, leisure and recreation into the wider program of resilience training.

The Baltic Dry Index: cyclicalities, forecasting and hedging strategies

The cyclical properties of the Baltic Dry Index (BDI) and their implications for forecasting performance are investigated. We find that changes in the BDI can lead to permanent shocks to trade of major exporting economies. In our forecasting exercise, we show that commodities and trigonometric regression can lead to improved predictions and then use our forecasting results to perform an investment exercise and to show how they can be used for improved risk management in the freight sector.

Holidays in the Danger Zone: Entanglements of War and Tourism

A timely, and uniquely historical, look at how war turns soldiers, and all of us, into tourists. Holidays in the Danger Zone exposes the mundane and everyday entanglements between two seemingly opposed worlds—warfare and tourism. Debbie Lisle shows how a tourist sensibility shapes the behavior of soldiers in warespecially the experiences of Western military forces in “exotic” settings. This includes not only R&R but also how battlefields themselves become landscapes of leisure and tourism. It further explores how a military sensibility shapes the development of tourism in the postwar context, from “Dark Tourism” (engaging with displays of conflict and atrocity) to exhibitions of conflict in museums and at memorial sites, as well as in advertising, film, journals, guidebooks, blogs, and photography. Focused on how war and tourism reinforce prevailing modes of domination, Holidays in the Danger Zone critically examines the long historical arc of the war-tourism nexus from nineteenth-century imperialism to World War I and World War II, from the Cold War to globalization and the War on Terror.