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Methode de prevision --- Serie chronologique --- Methode de lissage --- Modele de holt --- Modele de holt-winters --- Modele de harrison --- Methode de box et jenkins --- Hassan ii --- Methode de prevision --- Serie chronologique --- Methode de lissage --- Modele de holt --- Modele de holt-winters --- Modele de harrison --- Methode de box et jenkins --- Hassan ii

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As Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) evolved into a global pandemic, assessments of coronavirus disease 19 (COVID-19) patients have presented health conditions including, in many cases, a mild to severe loss of smell and tasting abilities among patients. Initial work has shown short and likely longer term negative effects on the human senses, with some indications of effects on consumer preferences; however, as of yet, very little is known about the impacts on eating behaviours and consequent longer term effects on appetite. The aim of this Special Issue anthology was, for the first time, to bring together researchers with key insights into how COVID-19 has impacted appetite and eating behaviours from the fundamental to the applicable, as assessed by human sensory perception. Thus, research is included that explores various themes, from the basic effects on the senses, to changes in consumer preferences, all the way to how and why COVID-19 has changed consumer behaviours in relation to food and eating in the longer term. Overall, we wished to document and bring together key research in the sensory and consumer space with respect to COVID-19, with the overall aim to highlight and ensure this research has a lasting impact regarding future understandings of measures developed to help and treat people affected during the ongoing pandemic.

lockdown --- COVID-19 --- coronavirus --- food choice --- food purchase --- food waste --- impulse buying --- food consumption --- mental health --- emotional eating --- sensory function --- chemosensory dysfunction --- perception --- appetite --- well-being --- pleasure --- recovery --- interview --- sensory perception --- eating behaviour --- self-reports --- food prices --- Eurozone --- Holt–Winters model --- green food --- purchase intention --- TPB --- E-TPB --- Chinese consumer --- consumer preference --- COVID-19 lockdown --- food preferences --- risk preference --- risk perceptions --- food purchasing behavior --- food consumption behavior --- sustainable behavior --- dietary behavior --- beverage consumption --- coffee --- tea --- online food delivery service --- COVID-19 pandemic --- technology acceptance --- trust --- enjoyment --- social influence --- young population --- food perception --- risk perception --- food safety --- Belgium --- consumer behaviour --- food service sector --- safety measures --- transparency --- olfactory distortions --- parosmia --- trigger foods --- disgust --- valence --- n/a --- Holt-Winters model

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This reprint introduces advanced prediction models focused on power load forecasting. Models based on artificial intelligence and more traditional approaches are shown, demonstrating the real possibilities of use to improve prediction in this field. Models of LSTM neural networks, LSTM networks with a SESDA architecture, in even LSTM-CNN are used. On the other hand, multiple seasonal Holt-Winters models with discrete seasonality and the application of the Prophet method to demand forecasting are presented. These models are applied in different circumstances and show highly positive results. This reprint is intended for both researchers related to energy management and those related to forecasting, especially power load.

Research & information: general --- Physics --- Prophet model --- Holt–Winters model --- long-term forecasting --- peak load --- prophet model --- multiple seasonality --- time series --- demand --- load --- forecast --- DIMS --- irregular --- galvanizing --- short-term electrical load forecasting --- machine learning --- deep learning --- statistical analysis --- parameters tuning --- CNN --- LSTM --- short-term load forecast --- Artificial Neural Network --- deep neural network --- recurrent neural network --- attention --- encoder decoder --- online training --- bidirectional long short-term memory --- multi-layer stacked --- neural network --- short-term load forecasting --- power system

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As Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) evolved into a global pandemic, assessments of coronavirus disease 19 (COVID-19) patients have presented health conditions including, in many cases, a mild to severe loss of smell and tasting abilities among patients. Initial work has shown short and likely longer term negative effects on the human senses, with some indications of effects on consumer preferences; however, as of yet, very little is known about the impacts on eating behaviours and consequent longer term effects on appetite. The aim of this Special Issue anthology was, for the first time, to bring together researchers with key insights into how COVID-19 has impacted appetite and eating behaviours from the fundamental to the applicable, as assessed by human sensory perception. Thus, research is included that explores various themes, from the basic effects on the senses, to changes in consumer preferences, all the way to how and why COVID-19 has changed consumer behaviours in relation to food and eating in the longer term. Overall, we wished to document and bring together key research in the sensory and consumer space with respect to COVID-19, with the overall aim to highlight and ensure this research has a lasting impact regarding future understandings of measures developed to help and treat people affected during the ongoing pandemic.

Research & information: general --- Biology, life sciences --- lockdown --- COVID-19 --- coronavirus --- food choice --- food purchase --- food waste --- impulse buying --- food consumption --- mental health --- emotional eating --- sensory function --- chemosensory dysfunction --- perception --- appetite --- well-being --- pleasure --- recovery --- interview --- sensory perception --- eating behaviour --- self-reports --- food prices --- Eurozone --- Holt-Winters model --- green food --- purchase intention --- TPB --- E-TPB --- Chinese consumer --- consumer preference --- COVID-19 lockdown --- food preferences --- risk preference --- risk perceptions --- food purchasing behavior --- food consumption behavior --- sustainable behavior --- dietary behavior --- beverage consumption --- coffee --- tea --- online food delivery service --- COVID-19 pandemic --- technology acceptance --- trust --- enjoyment --- social influence --- young population --- food perception --- risk perception --- food safety --- Belgium --- consumer behaviour --- food service sector --- safety measures --- transparency --- olfactory distortions --- parosmia --- trigger foods --- disgust --- valence --- lockdown --- COVID-19 --- coronavirus --- food choice --- food purchase --- food waste --- impulse buying --- food consumption --- mental health --- emotional eating --- sensory function --- chemosensory dysfunction --- perception --- appetite --- well-being --- pleasure --- recovery --- interview --- sensory perception --- eating behaviour --- self-reports --- food prices --- Eurozone --- Holt-Winters model --- green food --- purchase intention --- TPB --- E-TPB --- Chinese consumer --- consumer preference --- COVID-19 lockdown --- food preferences --- risk preference --- risk perceptions --- food purchasing behavior --- food consumption behavior --- sustainable behavior --- dietary behavior --- beverage consumption --- coffee --- tea --- online food delivery service --- COVID-19 pandemic --- technology acceptance --- trust --- enjoyment --- social influence --- young population --- food perception --- risk perception --- food safety --- Belgium --- consumer behaviour --- food service sector --- safety measures --- transparency --- olfactory distortions --- parosmia --- trigger foods --- disgust --- valence

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The analysis and modeling of time series is of the utmost importance in various fields of application. This Special Issue is a collection of articles on a wide range of topics, covering stochastic models for time series as well as methods for their analysis, univariate and multivariate time series, real-valued and discrete-valued time series, applications of time series methods to forecasting and statistical process control, and software implementations of methods and models for time series. The proposed approaches and concepts are thoroughly discussed and illustrated with several real-world data examples.

time series --- anomaly detection --- unsupervised learning --- kernel density estimation --- missing data --- multivariate time series --- nonstationary --- spectral matrix --- local field potential --- electric power --- forecasting accuracy --- machine learning --- extended binomial distribution --- INAR --- thinning operator --- time series of counts --- unemployment rate --- SARIMA --- SETAR --- Holt–Winters --- ETS --- neural network autoregression --- Romania --- integer-valued time series --- bivariate Poisson INGARCH model --- outliers --- robust estimation --- minimum density power divergence estimator --- CUSUM control chart --- INAR-type time series --- statistical process monitoring --- random survival rate --- zero-inflation --- cointegration --- subspace algorithms --- VARMA models --- seasonality --- finance --- volatility fluctuation --- Student’s t-process --- entropy based particle filter --- relative entropy --- count data --- time series analysis --- Julia programming language --- ordinal patterns --- long-range dependence --- multivariate data analysis --- limit theorems --- integer-valued moving average model --- counting series --- dispersion test --- Bell distribution --- count time series --- estimation --- overdispersion --- multivariate count data --- INGACRCH --- state-space model --- bank failures --- transactions --- periodic autoregression --- integer-valued threshold models --- parameter estimation --- models