Modeling CO2 Emissions of Top 5 Emitters with Combination Technique

2012 ◽  
Vol 616-618 ◽  
pp. 1143-1147
Author(s):  
Wei Sun ◽  
Jing Min Wang ◽  
Jun Jie Kang
Keyword(s):  
Forecast Error ◽  
Combination Method ◽  
Future Projection ◽  
Forecasting Accuracy ◽  
Combination Technique ◽  
Combination Forecast ◽  
Forecast Models ◽  
Model Time Series ◽  
Grey Verhulst Model ◽  
Better Than

In this paper, the performance of combination forecast methods for CO2 emissions prediction is investigated. Linear model, time series model, GM (1, 1) model and Grey Verhulst model are selected in study as the separate models. And, four kinds of combination forecast models, i.e. the equivalent weight (EW) combination method, variance-covariance (VACO) combination method, regression combination (R) method, and discounted mean square forecast error (MSFE) method are chosen to employ for top 5 CO2 emitters. The forecasting accuracy is compared between these combination models and single models. This research suggests that the combination forecasts are almost certain to outperform the worst individual forecasts and maybe even better than most individual ones. Furthermore the combination forecasts can avoid the risk of model choosing in future projection. For CO2 emissions forecast with many uncertain factors in the future, combining the single forecast would be safer in such forecasting situations.

scholarly journals Tail-Heaviness, Asymmetry, and Profitability Forecasting by Quantile Regression

2020 ◽  
Author(s):  
Hui Tian ◽  
Andrew Yim ◽  
David P. Newton
Keyword(s):  
Quantile Regression ◽  
Forecast Error ◽  
Simulated Data ◽  
Ordinary Least Squares ◽  
Shape Effect ◽  
Forecasting Accuracy ◽  
Forecasting Performance ◽  
Cash Flow Forecasting ◽  
Positive Effect ◽  
Better Than

We show that quantile regression is better than ordinary-least-squares (OLS) regression in forecasting profitability for a range of profitability measures following the conventional setup of the accounting literature, including the mean absolute forecast error (MAFE) evaluation criterion. Moreover, we perform both a simulated-data and an archival-data analysis to examine how the forecasting performance of quantile regression against OLS changes with the shape of the profitability distribution. Considering the MAFE and mean squared forecast error (MSFE) criteria together, we see that the quantile regression is more accurate relative to OLS when the profitability to be forecast has a heavier-tailed distribution. In addition, the asymmetry of the profitability distribution has either a U-shape or an inverted-U-shape effect on the forecasting accuracy of quantile regression. An application of the distributional shape analysis framework to cash flow forecasting demonstrates the usefulness of the framework beyond profitability forecasting, providing additional empirical evidence on the positive effect of tail-heaviness and supporting the notion of an inverted-U-shape effect of asymmetry. This paper was accepted by Shiva Rajgopal, accounting.

The Met Office operational global ocean forecast system FOAM-ORCA12

2021 ◽  
Author(s):  
Ana Barbosa Aguiar ◽  
Jennifer Waters ◽  
Martin Price ◽  
Gordon Inverarity ◽  
Christine Pequignet ◽  
...  
Keyword(s):  
Computational Cost ◽  
Absolute Error ◽  
Global Ocean ◽  
Background Error ◽  
Verification Methods ◽  
Climate Simulations ◽  
Forecast Models ◽  
The Mean ◽  
Lower Continuous ◽  
Better Than

<div> <p>The importance of oceans for atmospheric forecasts as well as climate simulations is being increasingly recognised with the advent of coupled ocean / atmosphere forecast models. Having comparable resolutions in both domains maximises the benefits for a given computational cost. The Met Office has recently upgraded its operational global ocean-only model from an eddy permitting 1/4 degree tripolar grid (ORCA025) to the eddy resolving 1/12 degree ORCA12 configuration while retaining 1/4 degree data assimilation. </p> </div><div> <p>We will present a description of the ocean-only ORCA12 system, FOAM-ORCA12, alongside some initial results. Qualitatively, FOAM-ORCA12 seems to represent better (than FOAM-ORCA025) the details of mesoscale features in SST and surface currents. Overall, traditional statistical results suggest that the new FOAM-ORCA12 system performs similarly or slightly worse than the pre-existing FOAM-ORCA025. However, it is known that comparisons of models running at different resolutions suffer from a double penalty effect, whereby higher-resolution models are penalised more than lower-resolution models for features that are offset in time and space. Neighbourhood verification methods seek to make a fairer comparison using a common spatial scale for both models and it can be seen that, as neighbourhood sizes increase, ORCA12 consistently has lower continuous ranked probability scores (CRPS) than ORCA025. CRPS measures the accuracy of the pseudo-ensemble created by the neighbourhood method and generalises the mean absolute error measure for deterministic forecasts. </p> </div><div> <p>The focus over the next year will be on diagnosing the performance of both the model and assimilation. A planned development that is expected to enhance the system is the update of the background-error covariances used for data assimilation. </p> </div>

scholarly journals Determination of Construction Parameters of Porous Ultra-Thin Overlays Based on Laboratory Compaction Studies

Materials ◽  
2020 ◽  
Vol 13 (20) ◽  
pp. 4496
Author(s):  
Jiahao Tian ◽  
Sang Luo ◽  
Ziming Liu ◽  
Xu Yang ◽  
Qing Lu
Keyword(s):  
Asphalt Concrete ◽  
Asphalt Mixture ◽  
Asphalt Mixtures ◽  
Combination Method ◽  
Compaction Temperature ◽  
Pavement Maintenance ◽  
Energy Equivalence ◽  
New Type ◽  
Better Than

To address the severe distresses of asphalt pavement, a new type of pavement maintenance treatment, porous ultra-thin overlay (PUTO) with small particle size was proposed. The PUTO has a thickness of 1.5–2.5 cm and a large void ratio of 18–25%. As a newly asphalt mixture, the structure characteristics differ from poor traditional pavement. Therefore, it is necessary to investigate the fabrication schemes in laboratory and on-site, respectively. In this study, the optimal fabrication schemes, including compaction temperature and number of blows of PUTO were determined based on Cantabro test and volumetric parameters. Then, the corresponding relationship between laboratory and on-site compaction work was then established based on the energy equivalent principle. On this basis, the numbers of on-site rolling passes and the combination method were calculated. The results show that increased compaction temperature and number of blows reduce the height and enhance the compaction of the Marshall sample. With the same temperature and number of blows, the raveling resistance of coarse gradation, Pavement Asphalt Concrete-1 (PAC-1) is better than that of fine gradation, Pavement Asphalt Concrete-2 (PAC-2), and the increased asphalt viscosity significantly improves the raveling resistance of the asphalt mixture. To ensure the scattering resistance and volumetric characteristic, the initial compaction temperature of the PAC-1 and PAC-2 should not be lower than 150 °C and 165 °C, respectively. Then, the laboratory compaction work and on-site compaction work were calculated and converted based on the principle of energy equivalence. Consequently, the on-site compaction combination of rolling machines for four asphalt mixtures was determined. According to the volumetric parameters, the paving test section proved that the construction temperature and the on-site rolling combination determined by laboratory tests are reasonable, and ultra-thin overlay has good structural stability, drainage, and skid resistance.

scholarly journals Prediction on New Ship Orders on the Basis of Combination Model

2012 ◽  
Vol 6-7 ◽  
pp. 1226-1230
Author(s):  
Si Qin Yu ◽  
Yi Ye Xiang ◽  
Yuan Tao Jiang
Keyword(s):  
Forecast Model ◽  
Support Vector ◽  
System Support ◽  
Forecast Method ◽  
Grey System ◽  
Shipbuilding Industry ◽  
Combination Forecast ◽  
Main Target ◽  
Accuracy Of Prediction ◽  
Better Than

When the new ship orders decline deeply and the shipbuilding capability is releasing quickly, how to guarantee the accuracy of prediction of new ship orders becomes the main target for shipbuilding corporate. This paper aims to predict the future demand of new ship with the help of combination forecast model that consist of grey system, support vector machine and artificial neural network. The result showed that combination forecast method is better than single usage of other three methods. The prediction result of new ship orders could provide some useful reference for the development of the shipbuilding industry.

Expected future performance of salmon abundance forecast models with varying complexity

2015 ◽  
Vol 72 (4) ◽  
pp. 557-569 ◽  
Author(s):  
Arliss J. Winship ◽  
Michael R. O’Farrell ◽  
William H. Satterthwaite ◽  
Brian K. Wells ◽  
Michael S. Mohr
Keyword(s):  
Fishery Management ◽  
Oncorhynchus Tshawytscha ◽  
Forecast Error ◽  
Model Complexity ◽  
Forecast Performance ◽  
Environmental Models ◽  
Future Performance ◽  
Management Objectives ◽  
Model Selection Uncertainty ◽  
Forecast Models

We evaluated the scope for improving abundance forecasts for fishery management using Sacramento River fall Chinook salmon (Oncorhynchus tshawytscha) as a case study. A range of forecast models that related the Sacramento Index (SI; an index of adult ocean abundance) to jack (estimated age 2) spawning escapement the previous year were considered. Alternative models incorporated effects of density dependence, local environmental conditions, the abundance of the previous cohort, and trends or autocorrelation in the jack-to-SI relationship. Forecast performance was assessed in terms of bias, accuracy, ability to track trends in the SI, and management objectives. Several models achieved higher accuracy than the model used for management, but no single model performed best across all criteria, and substantial forecast error remained across all approaches considered. Environmental models generally performed better than the management model, but there were differences in the relative importance of individual environmental variables over time and among model formulations. Accounting for model selection uncertainty in environmental models decreased their forecast performance. Simpler models often had similar or better performance than environmental models. In particular, the model incorporating temporally autocorrelated errors demonstrated potential for modest forecast improvement with relatively little additional model complexity.

Towards onset: shades of ENSO skill

2020 ◽  
Author(s):  
Dougal Squire ◽  
James Risbey
Keyword(s):  
El Niño ◽  
Statistical Models ◽  
El Nino ◽  
Southern Oscillation ◽  
Skill Score ◽  
Forecast Skill ◽  
The North ◽  
Forecast Models ◽  
The Impact ◽  
Better Than

<p>Climate forecast skill for the El Nino-Southern Oscillation (ENSO) is better than chance, but has increased little in recent decades. Further, the relative skill of dynamical and statistical models varies in skill assessments, depending on choices made about how to evaluate the forecasts. Using a suite of models from the North American Multi-Model Ensemble (NMME) archive we outline the consequences for skill of how the bias corrections and forecast anomalies are formed. We show that the method for computing forecast anomalies is such a critical part of the provenance of a skill score that any score for forecast anomalies lacking clarity about the method is open to wide interpretation. Many assessments of hindcast skill are likely to be overestimates of attainable forecast skill because the hindcast anomalies are informed by observations over the period assessed that would not be available to a real forecast. The relative skill rankings of forecast models can change between hindcast and forecast systems because the impact of model bias on skill is sensitive to the ways in which forecast anomalies are formed. Dynamical models are found to be more skillful than simple statistical models for forecasting the onset of El Nino events.</p>

An Empirical Comparison of Logit Choice Models with Discrete versus Continuous Representations of Heterogeneity

2002 ◽  
Vol 39 (4) ◽  
pp. 479-487 ◽  
Author(s):  
Rick L. Andrews ◽  
Andrew Ainslie ◽  
Imran S. Currim
Keyword(s):  
Choice Models ◽  
Parameter Estimates ◽  
Forecasting Accuracy ◽  
Parameter Recovery ◽  
Household Level ◽  
Discrete Representations ◽  
Analysis Models ◽  
Data Context ◽  
Continuous Representations ◽  
Better Than

Currently, there is an important debate about the relative merits of models with discrete and continuous representations of consumer heterogeneity. In a recent JMR study, Andrews, Ansari, and Currim (2002 ; hereafter AAC) compared metric conjoint analysis models with discrete and continuous representations of heterogeneity and found no differences between the two models with respect to parameter recovery and prediction of ratings for holdout profiles. Models with continuous representations of heterogeneity fit the data better than models with discrete representations of heterogeneity. The goal of the current study is to compare the relative performance of logit choice models with discrete versus continuous representations of heterogeneity in terms of the accuracy of household-level parameters, fit, and forecasting accuracy. To accomplish this goal, the authors conduct an extensive simulation experiment with logit models in a scanner data context, using an experimental design based on AAC and other recent simulation studies. One of the main findings is that models with continuous and discrete representations of heterogeneity recover household-level parameter estimates and predict holdout choices about equally well except when the number of purchases per household is small, in which case the models with continuous representations perform very poorly. As in the AAC study, models with continuous representations of heterogeneity fit the data better.

scholarly journals Optimal forecasting accuracy using Lp-norm combination

METRON ◽  
2021 ◽  
Author(s):  
Massimiliano Giacalone
Keyword(s):  
Real Data ◽  
Optimization Techniques ◽  
Combination Method ◽  
Forecast Combination ◽  
Forecasting Accuracy ◽  
Lp Norm ◽  
Combination Methods ◽  
Historical Series ◽  
Non Gaussian ◽  
Standard Regression

AbstractA well-known result in statistics is that a linear combination of two-point forecasts has a smaller Mean Square Error (MSE) than the two competing forecasts themselves (Bates and Granger in J Oper Res Soc 20(4):451–468, 1969). The only case in which no improvements are possible is when one of the single forecasts is already the optimal one in terms of MSE. The kinds of combination methods are various, ranging from the simple average (SA) to more robust methods such as the one based on median or Trimmed Average (TA) or Least Absolute Deviations or optimization techniques (Stock and Watson in J Forecast 23(6):405–430, 2004). Standard regression-based combination approaches may fail to get a realistic result if the forecasts show high collinearity in several situations or the data distribution is not Gaussian. Therefore, we propose a forecast combination method based on Lp-norm estimators. These estimators are based on the Generalized Error Distribution, which is a generalization of the Gaussian distribution, and they can be used to solve the cases of multicollinearity and non-Gaussianity. In order to demonstrate the potential of Lp-norms, we conducted a simulated and an empirical study, comparing its performance with other standard-regression combination approaches. We carried out the simulation study with different values of the autoregressive parameter, by alternating heteroskedasticity and homoskedasticity. On the other hand, the real data application is based on the daily Bitfinex historical series of bitcoins (2014–2020) and the 25 historical series relating to companies included in the Dow Jonson, were subsequently considered. We showed that, by combining different GARCH and the ARIMA models, assuming both Gaussian and non-Gaussian distributions, the Lp-norm scheme improves the forecasting accuracy with respect to other regression-based combination procedures.

scholarly journals Determination of Construction Parameters of Porous Ultra-Thin Overlays Based on Laboratory Compaction Studies

2020 ◽  
Author(s):  
Jiahao Tian ◽  
Sang Luo ◽  
Ziming Liu ◽  
Xu Yang ◽  
Qing Lu
Keyword(s):  
Void Ratio ◽  
Asphalt Mixture ◽  
Asphalt Mixtures ◽  
Combination Method ◽  
Compaction Temperature ◽  
Pavement Maintenance ◽  
Energy Equivalence ◽  
New Type ◽  
Better Than

To address the severe distresses of asphalt pavement, a new type of pavement maintenance treatment, porous ultra-thin overlay (PUTO) with small particle size was proposed. The PUTO has a thickness of 1.5~2.5 cm and a large void ratio of 18~25%. As a newly asphalt mixture, the structure characteristics differ from traditional pavement. Therefore, it is necessary to investigated the fabrication schemes in laboratory and on-site, respectively. In this study, the optimal fabrication schemes, including compaction temperature and number of blows of PUTO were determined based on Cantabro test and volumetric parameters. Then, the corresponding relationship between laboratory and on-site compaction work was then established based on the energy equivalent principle. On this basis, the numbers of on-site rolling passes and the combination method were calculated. The results show that increased compaction temperature and number of blows reduce the height and enhance the compactness of the Marshall sample. With the same temperature and number of blows, the scattering resistance of coarse gradation (PAC-1) is better than that of fine gradation (PAC-2), and the increased asphalt viscosity significantly improves the scattering resistance of the asphalt mixture. To ensure the scattering resistance and volumetric characteristic, the initial compaction temperature of the PAC-1 and PAC-2 should not be lower than 150 °C and 165 °C, respectively. Then, the laboratory compaction work and on-site compaction work were calculated and converted based on the principle of energy equivalence. Consequently, the on-site compaction combination of rolling machines for four asphalt mixtures was determined. According to the volumetric parameters, the paving test section proved that the construction temperature and the on-site rolling combination determined by laboratory tests are reasonable, and ultra-thin overlay has good structural stability, drainage and skid resistance.

scholarly journals Status kebersihan gigi dan mulut berdasarkan cara menyikat gigi dengan teknik kombinasi pada anak kidal dan non-kidal

e-GIGI ◽  
2017 ◽  
Vol 5 (1) ◽  
Author(s):  
Novany Lumempouw ◽  
Christy N. Mintjelungan ◽  
Kustina Zuliari
Keyword(s):  
Oral Hygiene ◽  
Tooth Brushing ◽  
Cross Sectional ◽  
Purposive Sampling ◽  
Combination Technique ◽  
North Sulawesi ◽  
Left Handed ◽  
Cross Sectional Design ◽  
The Right ◽  
Better Than

Abstract: During the developmental stage, children begin to do a variety of activities including tooth brushing. Generally, children use their right hands dominantly to do their activities (right-handed), however, there are also children who use their left hands (left-handed) dominantly. This study was aimed to assess the oral hygiene status based on tooth brushing with a combination technique among left-handed and right-handed children. This was a descriptive study with a cross-sectional design. Population study consisted of left-handed and right-handed children at Kalawat, North Minahasa, North Sulawesi province. Respondents were 60 children consisted of 30 left-handed children and 30 right-handed children obtained by using the purposive sampling method. Data were obtained by using checking form of oral hygiene status. The results showed that oral hygiene status of most left-handed and right-handed children was in good category. The average of OHI-S score of the left-handed children before tooth brushing was 0.7 and after tooth brushing was 0.3, whereas, of the right-handed children, the average of OHI-S score before tooth brushing was 0.6 and after tooth brushing was 0.2. Conclusion: Oral hygiene status of right-handed children who brushed their teeth with a combination technique was better than of the left-handed children. Keywords: oral hygiene status, left-handed children, right-handed children, tooth brushing, combination techniqueAbstrak: Seiring berjalannya tahap perkembangan, anak-anak mulai melakukan aktivitas termasuk menyikat gigi. Umumnya anak dominan melakukan aktivitas menggunakan tangan kanan (non-kidal) tetapi ada juga yang dominan melakukan aktivitas menggunakan tangan kiri (kidal). Penelitian ini bertujuan untuk mendapatkan status kebersihan gigi dan mulut nerdasrkan cara menyikat gigi dengan teknik kombinasi pada anak kidal dan non-kidal. Jenis penelitian ialah deskriptif dengan desain potong lintang. Populasi penelitian ialah anak kidal dan non-kidal di Kecamatan Kalawat Kabupaten Minahasa Utara Provinsi Sulawesi Utara. Jumlah responden sebanyak 60 orang anak terdiri dari 30 anak kidal dan 30 anak non-kidal diambil dengan metode purposive sampling. Pengumpulan data menggunakan formulir pemeriksaan status kebersihan gigi dan mulut. Hasil penelitian menunjukkan status kebersihan gigi dan mulut pada anak kidal dan anak non-kidal sebagian besar memiliki kategori baik. Rerata skor OHI-S anak kidal sebelum menyikat gigi yaitu 0,7 dan sesudah menyikat gigi 0,3 sedangkan pada anak non-kidal rerata skor OHI-S sebelum menyikat gigi 0,6 dan sesudah menyikat gigi 0,2. Simpulan: Status kebersihan gigi dan mulut berdasarkan cara menyikat gigi menggunakan teknik kombinasi pada anak non-kidal lebih baik dibandingkan pada anak kidal.Kata kunci: status kebersihan gigi dan mulut, anak kidal, anak non-kidal, menyikat gigi teknik kombinasi.

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