ライフサイエンスコーパス: forecasting

1 in tissue and may also be useful for seizure forecasting.

2 high-precision applications like flash flood forecasting.

3 ights into flare physics and improving flare forecasting.

4 ntal entropy barrier for disease time series forecasting.

5 earity, and was the most accurate method for forecasting.

6 xtensive post hoc investigation into seizure forecasting.

7 logical invasions that may aid in ecological forecasting.

8 rvational content but supports more accurate forecasting.

9 reproducible research that advanced seizure forecasting.

10 s of discrimination, construct validity, and forecasting.

11 se forecasting more challenging than weather forecasting.

12 to the field, setting the stage for exposure forecasting.

13 t better models are needed to improve dengue forecasting.

14 of evolution, regulation, and computational forecasting.

15 , planning healthcare capacity, and epidemic forecasting.

16 hquakes is required for effective earthquake forecasting.

17 on dynamics models - what we call structural forecasting.

18 arking; and data assimilation and ecological forecasting.

19 have not yet been used for seasonal hypoxia forecasting.

20 onfronted this epidemic, as well as in those forecasting a possible second outbreak.

21 was to assess how the DBN could improve the forecasting ability of infectious diseases.

22 ch that disentangles different components of forecasting ability using metrics that separately assess

23 x models with many parameters can compromise forecasting ability.

24 The quantitative forecasting accuracy (95% confidence interval [CI]) for

25 aditional data sources are needed to improve forecasting accuracy and its integration with real-time

26 ack of a gold-standard tuberculosis DDT, the forecasting accuracy of a completely unreliable tool was

27 1) ), were evaluated for their usability for forecasting adult heat tolerance, measured as the vegeta

28 thogen, and how monitoring and computational forecasting affect protocols and efficiency of control.

29 er than choice itself.SIGNIFICANCE STATEMENT Forecasting aggregate behavior with individual neural da

30 he planetary boundary layer (PBL) is key for forecasting air quality and estimating greenhouse gas (G

31 In this work, we present a forecasting algorithm that exploits the dimensionality o

32 able indicator that could be used in seizure forecasting algorithms.

33 echnologies could accelerate the adoption of forecasting among public health practitioners, improve e

34 laws to reduce data dependence in ecological forecasting and accurately predict extreme events beyond

35 e, we detail three regional-scale models for forecasting and assessing the course of the pandemic.

36 yme could be acted as a unique biomarker for forecasting and diagnosing certain diseases.

37 These principles inform evolutionary forecasting and have relevance to interpreting the diver

38 rgency of incorporating mechanism into range forecasting and invasion management to understand how cl

39 high VPD on plant function, improvements in forecasting and long-term projections of climate impacts

40 pendence, volatility smiles, etc) to improve forecasting and management of complex adaptive systems.

41 have implications for flood susceptibility, forecasting and mitigation, including management of grou

42 improving the predictive ability of seasonal forecasting and modelling of long-range spatial connecti

43 hallenging issue of AMD and, more generally, forecasting and optimization of mineral leaching in indu

44 Targets for improvement include drug forecasting and procurement, and addressing provider rel

45 neously improve the quality of economic risk forecasting and reinforce individual government and dono

46 se new inferences are important for eruption forecasting and risk mitigation, and have significant im

47 arly vaccinated populations and thus improve forecasting and vaccination strategies to combat seasona

48 ing teams will continue to advance influenza forecasting and work to improve the accuracy and reliabi

49 lity poses greater challenges to operational forecasting and, consequently, greater coastal risk duri

50 h uses including virtual biobanking, disease forecasting, and adaption to other disease outbreaks.

51 play in advancing theory, improving hind and forecasting, and enabling problem solving and management

52 es and improving the processes of diagnosis, forecasting, and tracking of normal and pathological agi

53 dynamic boundary shape suggests that current forecasting approaches assuming a constant shape could b

54 s between research teams to develop ensemble forecasting approaches can bring measurable improvements

55 Forecasting assemblage-level responses to climate change

56 ith Recurrent Neural Networks in the task of forecasting bees' level of activity taking into account

57 ich severely restricts longer-term, accurate forecasting beyond boreal spring.

58 represents a major unresolved challenge for forecasting biosphere responses to global change.

59 Forecasting 'Black Swan' events in ecosystems is an impo

60 classification curves were well above chance forecasting, but did show a mean 6.54 +/- 2.45% (min, ma

61 nd computational methods for epidemiological forecasting, but here we consider a serious alternative

62 o reduce the local biases of the NSCS marine forecasting by as much as 28-31% (19-36%) in 24 h to 120

63 ametric statistical approach based on analog forecasting, called kernel analog forecasting (KAF), whi

64 ted phenomenon, and demonstrate that genetic forecasting can aid preparedness for impending viral inv

65 More accurate forecasting can help officials better respond to and pla

66 We also propose a method for forecasting case counts, which takes advantage of the co

67 icture of ENSO global impacts widely used by forecasting centers and atmospheric science textbooks ca

68 ur participation in a weekly prospective ILI forecasting challenge for the United States for the 2016

69 imple average of all models submitted to the forecasting challenge.

70 (CDC) has hosted an annual influenza season forecasting challenge.

71 ith team participation in previous influenza forecasting challenges and utilization of ensemble forec

72 ntion (CDC) has organized seasonal influenza forecasting challenges since the 2013/2014 season.

73 light the value of a multi-model approach in forecasting climate change impacts and uncertainties and

74 rces, atmospheric and oceanic transport, and forecasting climate change impacts through modeling.

75 itation, temperature, etc.), which underlies forecasting climate change impacts.

76 utionary history of a cancer is important in forecasting clinical outlook.

77 sted drugs, and specific predictors aimed at forecasting clinical response to treatment with four spe

78 In the past 3 decades, the weather forecasting community has made significant advances in d

79 A seizure forecasting competition was conducted on kaggle.com usin

80 ld probabilistic problems such as diagnosis, forecasting, computer vision, etc.

81 n western Micronesia. Our novel approach for forecasting coral growth into the future may be applicab

82 power, suggesting that advances in long-term forecasting could be exploited to markedly improve manag

83 olera and climate patterns coupled with ENSO forecasting could be used to notify countries in Africa

84 re important to develop accurate methods for forecasting dengue and ILI incidences.

85 odels with and without climate variables for forecasting dengue incidence in Mexico.

86 ime interest due to potential application in forecasting disease onset.

87 present a method based on deep learning for forecasting disruptions.

88 ent, urban pluvial flood-risk management and forecasting, drinking water and sewer network operation

89 Forecasting ecological responses to climate change, inva

90 of health findings with traditional weather forecasting efforts could be critical in the development

91 However, forecasting ENSO is one of the most difficult problems i

92 The average forecasting error was 9.22%.

93 prove the foresting results through reducing forecasting errors by 7%.

94 s that a Gaussian density, estimated on past forecasting errors, gives comparatively accurate uncerta

95 applications include political and economic forecasting, evaluating nuclear safety, public policy, t

96 rd the development of mechanistic models for forecasting evolution, highlight current limitations, an

97 ble to improve accuracy during a prospective forecasting exercise by coupling dynamics between region

98 A number of HWRF forecasting experiments were carried out at different ve

99 We also participated in two live forecasting experiments.

100 is important because of the implications for forecasting explosive eruptions and predicting their int

101 Forecasting explosive eruptions relies on using monitori

102 ogy in the 21st Century (Tox21) and exposure forecasting (ExpoCast) are generating mechanistic data t

103 ope of our approach, we applied it both in a forecasting fashion to predict the ILI trend of the 2016

104 rop growth and yield has important value for forecasting food production and prices and ensuring regi

105 f the familiar tercile framework of seasonal forecasting for the characterization of 21st-century pre

106 Our results imply that the real-time marine forecasting for the NSCS can largely benefit from a sust

107 Forecasting fracture locations in a progressively failin

108 roof-of-concept for implementing a circadian forecasting framework, and provide insight into new appr

109 We conclude that models forecasting future biodiversity changes should consider

110 sented here offer considerable potential for forecasting future conditions, highlight regions of conc

111 opics and subtropics, and Arctic tundra when forecasting future terrestrial carbon-climate feedback.

112 Additionally, migrants could benefit from forecasting future wind conditions, crossing on nights w

113 on-makers to frequently update (and consider forecasting) grid emissions factors.

114 However, progress toward reliable seizure forecasting has been hampered by lack of open access to

115 Forecasting healthcare utilization has the potential to

116 The forecasting horizon could be extended up to 3 days while

117 tions were increasingly unreliable at longer forecasting horizons.

118 effects of threatening human processes, and forecasting how threatened species might be distributed

119 Forecasting impacts of future climate change is an impor

120 The uneven forecasting improvements across targets hold even when "

121 and demonstrates the feasibility of seizure forecasting in canine and human epilepsy.media-1vid110.1

122 echanisms is of great importance for weather forecasting in general and extreme-event prediction in p

123 erformance than the historical baseline when forecasting incidence of influenza-like illness 1 wk, 2

124 we observed state-of-the-art performances in forecasting individual CKD onsets with different machine

125 Forecasting is beginning to be integrated into decision-

126 Eruption forecasting is inherently challenging in cascading scena

127 material-based disciplines for which failure forecasting is of central importance.

128 With a successful methodology toward tumor forecasting, it should be possible to integrate large tu

129 athematical models play an important role in forecasting its future dynamics.

130 Understanding influenza dynamics and forecasting its impact is fundamental for developing pre

131 on analog forecasting, called kernel analog forecasting (KAF), which avoids assumptions on the under

132 nd diverging methodologies of estimation and forecasting, leading to important differences in global

133 These findings imply that forecasting location-specific greenhouse gas payback tim

134 This paper uses three forecasting machines: (i) data assimilation, a technique

135 ed by assessing their predictive accuracy in forecasting malaria admissions at lead times of one to t

136 Our results have important implications for forecasting mangrove carbon dynamics and the persistence

137 Our forecasting method enables researchers and industry to e

138 We find that this forecasting method is robust and it outperforms logistic

139 pH variations a year in advance over a naive forecasting method, with the potential for skillful pred

140 d a modified nonparametric empirical density forecasting method.

141 In study 2, this "local forecasting" methodology outperformed participant evalua

142 and can be used in combination with existing forecasting methods and more comprehensive dengue models

143 our method outperforms existing time series forecasting methods in forecasting the dengue and ILI ca

144 ing performance of several empirical density forecasting methods, using the continuous ranked probabi

145 r, we show that cLV is as accurate as gLV in forecasting microbial trajectories in terms of relative

146 A linear forecasting model based on a logic matrix decision tree

147 Our results show that a good flu forecasting model can benefit from the augmentation of i

148 ty experiments with the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) and

149 hemical transport model-Weather Research and Forecasting model coupled with Chemistry version 3.5 (WR

150 The performance of the proposed forecasting model for cpRNFL is consistent across glauco

151 evaluated, such as The Weather Research and Forecasting Model meteorology predictions, emission inve

152 The best forecasting model of cpRNFL was obtained using 3 visits

153 We use the Weather Research and Forecasting Model with atmospheric chemistry (WRF-Chem)

154 n simulations using the Weather Research and Forecasting model with Chemistry based on a new fire emi

155 opic using the regional Weather Research and Forecasting model with European Center for Medium range

156 imitations of augmenting an already good flu forecasting model with internet-based nowcasts.

157 unt the uncertainty related to the choice of forecasting model.

158 temporal sampling, regardless of the adopted forecasting model.

159 Forecasting models (neural networks) predicted an increa

160 nt step towards improved accuracy of disease forecasting models and evaluation of disease control int

161 mphasize real-time testing and evaluation of forecasting models and facilitate the close collaboratio

162 standardize the collection and evaluation of forecasting models for influenza in the United States fo

163 nd demonstrates that the predictive power of forecasting models is improved by circadian information.

164 in Saskatchewan, Canada, is used to develop forecasting models of odor using chlorophyll a, turbidit

165 The forecasting models used multimodal patient information i

166 access should be incorporated into COVID-19 forecasting models when applied to low-income countries.

167 We developed an ensemble of 21 forecasting models, all of which probabilistically contr

168 ving satellites, and data-assimilating ocean forecasting models.

169 eillance data provide opportunity to develop forecasting models.

170 fields, such as human behavior, make disease forecasting more challenging than weather forecasting.

171 n this study, we developed novel methods for forecasting mortality, fertility, migration, and populat

172 On a variety of prediction tasks-forecasting new infections, the popularity of topics in

173 We applied an ensemble forecasting niche modelling approach to project the clim

174 Methods for forecasting nonaqueous solubility would be valuable for

175 led nutrient cycles, as well as modeling and forecasting nutrient controls over carbon-climate feedba

176 developed Bayesian spatiotemporal models for forecasting of age-specific mortality and life expectanc

177 f S. aureus populations could lead to better forecasting of antibiotic resistance and virulence of em

178 Accurate forecasting of cardiovascular disease mortality is cruci

179 Early forecasting of COVID-19 virus spread is crucial to decis

180 study was to investigate how to improve the forecasting of craniofacial unbalance risk during growth

181 condition to these outcomes would allow the forecasting of disease process and the tailoring of ther

182 's Disease to train a model for personalized forecasting of disease progression.

183 time data are valuable in the monitoring and forecasting of epidemics and outbreaks, it is evident th

184 invasive electroceuticals to enable accurate forecasting of epileptic seizures and therapy.

185 NTIFIC COMMENTARY ON THIS ARTICLE : Accurate forecasting of epileptic seizures has the potential to t

186 shed clinical predictors enable the accurate forecasting of functional decline.

187 Improved forecasting of future changes in SOM is needed to suppor

188 and monitoring of Kilauea enabled successful forecasting of hazardous events.

189 ation filtering forecast model for real-time forecasting of HFMD.

190 by age and sex to improve real-time targeted forecasting of hospitalization and critical care needs.

191 nstrate new capabilities, including accurate forecasting of microbial dynamics, prediction of stable

192 Experiments suggest that seasonal forecasting of ocean conditions important for fisheries

193 to such changes, therefore understanding and forecasting of precipitation variability is vital to bet

194 Our findings aid improved understanding and forecasting of Sahel drought, paramount for successful a

195 is study proposes a method for probabilistic forecasting of the disease incidences in extended range

196 rder-specific effects may allow for rational forecasting of the evolutionary dynamics of bacteria giv

197 our approach could lead to more informative forecasting of the seismic activity in seismogenic areas

198 Skilful seasonal forecasting of the surface climate in both Europe and No

199 orest model is also able to provide accurate forecasting of TON levels requiring treatment 12 weeks i

200 ment, national inventories of tOPV, detailed forecasting of tOPV needs, bOPV licensing, scaling up of

201 Accurate understanding and forecasting of traffic is a key contemporary problem for

202 can provide accurate, noninvasive, and early forecasting of ultimate outcomes for NSCLC patients rece

203 e where system nonlinearities limit accurate forecasting of unprecedented events due to poor extrapol

204 ive tool for spatially explicit tracking and forecasting of wildlife population dynamics at scales th

205 and scalable biomarkers (current and future forecasting) of AD pathology, and carry both therapeutic

206 Efforts to improve sea level forecasting on a warming planet have focused on determin

207 ortance of RI has been recognized in weather forecasting, our results demonstrate that RI also plays

208 ple with epilepsy might benefit from seizure forecasting over long horizons.

209 direct implications for improving heavy rain forecasting over the IMR, by developing realistic land c

210 ngs suggest that further improvements to flu forecasting, particularly seasonal targets, will need to

211 We evaluate the out-of-sample forecasting performance of several empirical density for

212 We report on forecasting performances and statistical significance of

213 s between temperature and precipitation when forecasting phenology over the coming decades.

214 Models forecasting plant community responses to global change i

215 ns of formation of known deposits as well as forecasting potential exploration targets.

216 her and climate modeling, we submit that the forecasting power of biophysical and biomathematical mod

217 Forecasting prevalence will allow health policy makers t

218 Yet mainstream macroeconomic forecasting rarely takes account of the risk of potentia

219 data can overestimate predictive skill when forecasting recruitment is part of the assessment proces

220 Forecasting refugee movements is important, as accurate

221 o explore novel approaches to monitoring and forecasting regional outbreaks as they happen or even be

222 Therefore, outbreak forecasting requires an integrative approach to modeling

223 ng and guiding agenda setting for ecological forecasting research and development.

224 ve enabled development of systems capable of forecasting seasonal influenza epidemics in temperate re

225 esearch has produced a number of methods for forecasting seasonal influenza outbreaks.

226 ct of large earthquakes requires signals for forecasting seismic events.

227 to model produced the outstanding result for forecasting seizure EEG with an error of 11.21%.

228 robability, and we tested the feasibility of forecasting seizures days in advance.

229 nd sleep quality (efficiency) provide future forecasting sensitivity to the rate of Abeta accumulatio

230 nowledge about migration potential is key to forecasting species distributions in changing environmen

231 n dynamic environments, which is critical in forecasting species distributions, as well as the divers

232 ) tend to have higher predictive accuracy in forecasting species range shifts than structurally simpl

233 ints to potential fundamental limitations in forecasting species shifting ranges without considering

234 etic variation is commonly ignored in models forecasting species vulnerability and biogeographical sh

235 iches are thus central for understanding and forecasting species' geographic distributions.

236 ntal to understanding resilience properties, forecasting state shifts, and developing effective manag

237 Forecasting storm impacts on these ecosystems requires c

238 Here, we build an accurate battery forecasting system by combining electrochemical impedanc

239 The hierarchical forecasting system can generate predictions for each vir

240 We present a next-generation monitoring and forecasting system for [Formula: see text]-borne disease

241 S national and regional levels, the proposed forecasting system generates improved predictions of bot

242 We created the web-based "Epicast" forecasting system which collects and aggregates epidemi

243 ers were assimilated into a real-time marine forecasting system, along with the assimilation of clima

244 However, forecasting systems are uncommon in most countries, with

245 nd attack rates, most existing process-based forecasting systems treat ILI as a single infectious age

246 at enable the development of epidemiological forecasting systems.

247 nditions to improve high-resolution regional forecasting systems.

248 across forecasting targets, with short-term forecasting targets seeing the largest improvements and

249 due to nowcasting, however, is uneven across forecasting targets, with short-term forecasting targets

250 ce for all considered public health-relevant forecasting targets.

251 in significant improvements in the skill for forecasting TC activity at daily and seasonal time-scale

252 sting challenges and utilization of ensemble forecasting techniques.

253 nd consistently outperform alternative naive forecasting techniques.

254 s and shoots have important consequences for forecasting terrestrial ecosystem responses to climate c

255 Despite their importance for eruption forecasting the causes of seismic rupture processes duri

256 Forecasting the consequences of climate change is contin

257 on with long-term water ageing are useful in forecasting the decline in strength of resin-dentine bon

258 existing time series forecasting methods in forecasting the dengue and ILI case counts.

259 thermal environment is largely ignored when forecasting the dynamics of non-native species.

260 iming of their adoption, opening the way for forecasting the effectiveness of future interventions.

261 innovation, as it also provides a context in forecasting the effects of climate change on the stabili

262 ction modes: a universal predictor, aimed at forecasting the efficacy of untested drugs, and specific

263 Forecasting the El Nino-Southern Oscillation (ENSO) has

264 Forecasting the equilibrium is complicated, especially a

265 d utility of a data-assimilation approach to forecasting the fate of cells undergoing EMT.

266 es respond to climate change is critical for forecasting the future dynamics and distribution of pest

267 at single species models may be adequate for forecasting the impacts of climate change in these commu

268 Capable of measuring and forecasting the impacts of social distancing, these mode

269 frastructure investment mechanisms, requires forecasting the market-clearing equilibrium.

270 Yet, forecasting the propagation of these predator-induced tr

271 standing the fate of gravel is important for forecasting the response of rivers to large influxes of

272 and interface, understanding soil mechanics, forecasting the risk of natural calamities, and so on.

273 Forecasting the spatiotemporal spread of infectious dise

274 Nonetheless, modeling and forecasting the spread of COVID-19 remains a challenge.

275 ers are struggling with these same issues in forecasting the spread of emerging diseases, such as Zik

276 Forecasting the state of health and remaining useful lif

277 rovide a quantitative physical mechanism for forecasting the strength and duration of bursty seasons

278 coral morphological traits may contribute to forecasting the structure of reef fish communities on no

279 tive interactions are rarely considered when forecasting the success or speed of expansion, in part b

280 k, and 3 wk ahead of available data and when forecasting the timing and magnitude of the seasonal pea

281 bidity and mortality around the world; thus, forecasting their impact is crucial for planning an effe

282 es, and thus provides a potential method for forecasting these events.

283 We use nonlinear forecasting to analyse 747 datasets of 228 species to fi

284 paving the way for equation-free mechanistic forecasting to be applied in management contexts.

285 a incidence in 2008-2017, then used Bayesian forecasting to examine an extensive range of scenarios.

286 mmunity is rising to the challenges posed by forecasting to help anticipate and guide the mitigation

287 Despite diverse efforts to develop forecasting tools including autoregressive time series,

288 disease events underscores a need to develop forecasting tools toward a more preemptive approach to o

289 ng the opening of new shipping lines and for forecasting trade volume on links.

290 free, data-driven approach for analyzing and forecasting traffic dynamics.

291 re need of a robust method for analyzing and forecasting traffic.

292 element method, is combined with time-series forecasting via auto regressive integrated moving averag

293 onal design of safer vaccine strains and for forecasting virulence of viruses.

294 In addition, for forecasting, we estimated a dynamic parametric model of

295 is little knowledge on how the difficulty of forecasting weather may be affected by anthropogenic cli

296 stem that exhibits a high degree of skill in forecasting wildfire probabilities and drought for 10-23

297 f the hypoxic zone that combines statistical forecasting with information from a 3D biogeochemical mo

298 Here we use the Weather Research and Forecasting (WRF) model over the western Gobi Desert to

299 he regional scale using the Weather Research Forecasting (WRF) model.

300 ergy flux method to the Weather Research and Forecasting (WRF) regional atmospheric model equipped wi