Analyse des séries temporelles

Bourbonnais, Régis; Terraza, Michel

doi:10.3917/dunod.bourb.2016.01

Cited by 10 publications

(8 citation statements)

References 0 publications

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“…The trend is defined as the long‐term variation and is one of the components of a time‐series along with seasonality and noise (i.e., the residuals component). The trend reflects the structural dynamic and was used to monitor long‐term movement (Bourbonnais & Terraza, ). Evidence of seasonality was sought using graphic analyses (autocorrelation function, partial autocorrelation function).…”

Section: Methodsmentioning

confidence: 99%

Antimicrobial resistance trends in Escherichia coli isolated from diseased food‐producing animals in France: A 14‐year period time‐series study

Boireau

Morignat

Cazeau

et al. 2017

Zoonoses and Public Health

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SummaryAntimicrobial resistance (AMR) among bacteria isolated from food-producing animals is a growing concern with implications for public health. AMR surveillance is essential to identify resistance trends and help in the design of effective and efficient control strategies. The aim of the study was to describe the antimicrobial susceptibility of pathogenic Escherichia coli isolated from three livestock productions in France (cattle, swine and poultry). The trend in resistance to the most commonly prescribed antibiotics in animal health was analysed as follows: amoxicillin (penicillin), spectinomycin or streptomycin (aminoglycoside), tetracycline and trimethoprim-sulfamethoxazole/Enrofloxacin and ceftiofur were also taken into ac- These results contribute to risk assessment and constitute objective evidence on which to evaluate the efficacy of control measures implemented to limit AMR occurrence. K E Y W O R D S

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Section: Methodsmentioning

confidence: 99%

Antimicrobial resistance trends in Escherichia coli isolated from diseased food‐producing animals in France: A 14‐year period time‐series study

Boireau

Morignat

Cazeau

et al. 2017

Zoonoses and Public Health

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“…In term of optimization algorithms comparison, it can be seen that the values of hyperparameters are selected optimally to find the minimum fitness function by using BGA. It outperforms some comparative methods (GA and DE) based on their accuracies as it can be illustrated inFigures 11,12,13,14 and Table5. DeepESN-PSO has achieve good results especially in long term forecasting.…”

mentioning

confidence: 84%

“…As a result, it has many models used depend a nature of problems. RNN (Recurrent neuron network) is a famous one for power forecasting as a time series data [14,15], because of his ability to memorize information from the previous states [14].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Deep Reservoir Computing with Binary Genetic Algorithm for Multi-Time Horizon Forecasting of Power Consumption

Bendali¹,

Saber²,

Boussetta³

et al. 2022

JESA

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The roll of consumption energy forecasting is very important to make planning of time-horizon strategy, and to mitigate a great energy management. As a result, improving the sustainability of energy, and creating a clean environment. Aiming to develop the forecasting of consumption energy in different time horizons, this work gives the results of a new hybrid method, which combine deep echo state network (DeepESN), with Binary genetic algorithm (BGA). DeepESN is an extension of Echo state network (ESN), which integrates the strong nonlinear time series processing capability (of ESN) with the advanced learning characteristic of the deep learning models. BGA is another version of genetic algorithm optimization methods that can be applied to find the best values of architecture hyperparameters of deep learning models, basesd on binary decoding of his chromosoms. In this work, we compared the accuracy and performance of proposed model DeepESN-BGA with other deep learning methods. It is found that DeepESN-BGA have a fast processing compared with other models. In addition, it gives best results based on error metrics, compared with DeepESN without BGA, and other deep learning models, in different time horizon forecasting. Proposed model has been compared also with DeepESN-DE, DeepESN-GA, and DeepESN-PSO aiming to evaluate the performance of BGA in term of deep learning optimization. DeepESN-BGA gives statistically good result compared with other hybrid models.

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“…Let us associate to Equation (2) the Equation Diff(t) = X(t) − X pred60 (t − 60) Three classic normality tests (see, e.g., Bourbonnais & Terraza (2010); Cryer & Chan (2008); Jarque & Bera (1987); Judge, Griffiths, Hill, Lütkepol & Lee (1988); Thode (2002)), namely…”

Section: Normality Testsmentioning

confidence: 99%

Solar energy production: Short-term forecasting and risk management

et al. 2016

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International audienceElectricity production via solar energy is tackled via short-term forecasts and risk management. Our main tool is a new setting on time series. It allows the definition of "confidence bands" where the Gaussian assumption, which is not satisfied by our concrete data, may be abandoned. Those bands are quite convenient and easily implementable. Numerous computer simulations are presented

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Analyse des séries temporelles

Cited by 10 publications

References 0 publications

Antimicrobial resistance trends in Escherichia coli isolated from diseased food‐producing animals in France: A 14‐year period time‐series study

Antimicrobial resistance trends in Escherichia coli isolated from diseased food‐producing animals in France: A 14‐year period time‐series study

Optimization of Deep Reservoir Computing with Binary Genetic Algorithm for Multi-Time Horizon Forecasting of Power Consumption

Solar energy production: Short-term forecasting and risk management

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