A Time Series Forecast of COVID-19 Infections, Recoveries and Fatalities in Nigeria

The study investigated COVID-19 pandemic infections, recoveries, and fatalities in Nigeria to forecast future values of infections, recoveries, and fatalities and thus ascertain the extent to which the pandemic appeared to be converging with time. The prediction of COVID-19 infections, recoveries, and fatalities was necessitated by the impact that the pandemic had exerted in world economies since its outbreak in late 2019. The quantitative method was employed, and a longitudinal research design was applied. Data were obtained from the Nigeria Centre for Disease Control (NCDC). The least-squares test and autoregressive distributed lag (ARDL) tests were performed to forecast infections, recoveries, and fatalities. The results of the predicted infections for the last five months of the year (August–December 2020) shows that the cases of infections will narrow down within the period. The need for policymakers to implement complete unlocking of the economy for speedy economic recovery was suggested, among others.

The YOPP Final Summit: Assessing Past and Forecasting Future Polar Prediction Research

The successes of YOPP from the presentations and keynote presentations included * a better understanding of the impact of key polar measurements (radiosondes and space-based instruments such as microwave radiometers), and recent advancements in the current NWP observing system, achieved through coordinated OSEs in both polar regions (e.g., Sandu et al. 2021);* enhanced understanding of the linkages between Arctic and midlatitude weather (e.g., Day et al. 2019);* advancements in the atmosphere–ocean–sea ice and atmosphere–land–cryosphere coupling in NWP, and in assessing and recognizing the added value of coupling in Earth system models (e.g., Bauer et al. 2016);* deployment of tailored polar observation campaigns to address yet-unresolved polar processes (e.g., Renfrew et al. 2019);* progress in verification and forecasting techniques for sea ice, including a novel headline score (e.g., Goessling and Jung 2018);* advances in process understanding and process-based evaluation with the establishment of the YOPPsiteMIP framework and tools (Svensson 2020);* better understanding of emerging societal and stakeholder needs in the Arctic and Antarctic (e.g., Dawson et al. 2017);and * innovative transdisciplinary methodologies for coproducing salient information services for various user groups (Jeuring and Lamers 2021). The YOPP Final Summit identified a number of areas worthy of prioritized research in the area of environmental prediction and services for the polar regions: * coupled atmosphere, sea ice, and ocean models with an emphasis on advanced parameterizations and enhanced resolution at which critical phenomena start to be resolved (e.g., ocean eddies);* improved definition and representation of stable boundary layer processes, including mixed-phase clouds and aerosols;incorporation of wave–ice–ocean interactions;* radiance assimilation over sea ice, land ice, and ice sheets;understanding of linkages between polar regions and lower latitudes from a prediction perspective;* exploring the limits of predictability of the atmosphere–cryosphere–ocean system;* an examination of the observational representativeness over land, sea ice, and ocean;better representation of the hydrological cycle;and * transdisciplinary work with the social science community around the use of forecasting services and operational decision-making to name but a few. The presentations and discussions at the YOPP Final Summit identified the major legacy elements of YOPP: the YOPPsiteMIP approach to enable easy comparison of collocated multivariate model and observational outputs with the aim of enhancing process understanding, the development of an international and multi-institutional community across many disciplines investigating aspects of polar prediction and services, the YOPP Data Portal3 (https://yopp.met.no/), and the education and training delivered to early-career polar researchers. Next steps Logistical issues, the COVID-19 pandemic, but also new scientific questions (e.g., the value of targeted observations in the Southern Hemisphere), as well as technical issues emerging toward the end of the YOPP Consolidation Phase, resulted in the decision to continue the following three YOPP activities to the end of 2023: (i) YOPP Southern Hemisphere (YOPP-SH);(ii) Model Intercomparison and Improvement Project (MIIP);of which YOPPSiteMIP is a critical element;and (iii) the Societal, Economics and Research Applications (PPP-SERA) Task Team.

Using Time Series Forecasting for Analysis of GDP Growth in India

Gross Domestic Product is one of the most important economic indicators of the country and its positive or negative growth indicates the economic development of the country. It is calculated quarterly and yearly at the end of the financial year. The GDP growth of India has seen fluctuations from last few decades after independence and reached as high as 10.25 in 2010 and declined to low of -5.23 in 1979. The GDP growth has witnessed a continuous decline in the past five years, taking it from 8.15 in 2015 to 1.87 in 2020.The lockdown imposed in the country to curb the spread of COVID-19 has caused massive slowdown in the economy of the country by affecting all major contributing sectors of the GDP except agricultural sector. To keep on track on the GDP growth is one of the parameters for deciding the economic policies of the country. In this study, we are analyzing and forecasting the GDP growth using the time series forecasting techniques Prophet and Arima model. This model can assist policy makers in framing policies or making decisions.

Aggregating Prophet and Seasonal Trend Decomposition for Time Series Forecasting of Italian Electricity Spot Prices

The cost of electricity and gas has a direct influence on the everyday routines of people who rely on these resources to keep their businesses running. However, the value of electricity is strongly related to spot market prices, and the arrival of winter and increased energy use owing to the demand for heating can lead to an increase in energy prices. Approaches to forecasting energy costs have been used in recent years;however, existing models are not yet robust enough due to competition, seasonal changes, and other variables. More effective modeling and forecasting approaches are required to assist investors in planning their bidding strategies and regulators in ensuring the security and stability of energy markets. In the literature, there is considerable interest in building better pricing modeling and forecasting frameworks to meet these difficulties. In this context, this work proposes combining seasonal and trend decomposition utilizing LOESS (locally estimated scatterplot smoothing) and Facebook Prophet methodologies to perform a more accurate and resilient time series analysis of Italian electricity spot prices. This can assist in enhancing projections and better understanding the variables driving the data, while also including additional information such as holidays and special events. The combination of approaches improves forecast accuracy while lowering the mean absolute percentage error (MAPE) performance metric by 18% compared to the baseline model.

Key Success Factors of Sustainable Organization for Traditional Manufacturing Industries: A Case Study in Taiwan

Even sustainable organizations have received overwhelming attention, but there is a lack of studies to explore the key success factors for sustainable traditional manufacturing based on expert opinions. The purpose of this study was to explore the key success factors for sustainable development in traditional industries through expert knowledge. In this study, the Delphi method was applied to construct the research framework with the most appropriate criteria. Moreover, we proposed an effective solution based on the Decision-Making Trial and Evaluation Laboratory (DEMATEL)-based Analytic Network Process (ANP) to determine the correlation and causality of these factors based on the decision laboratory method for multi-criteria decision-making. We also integrated the importance–performance analysis to illustrate the attributes improvement priorities. Our results show that managers and policy-makers should concentrate more on knowledge management to enhance the sustainability of organizations. Moreover, managers should keep teamwork and employee engagement at a high level to achieve the goal of organizations. Additionally, the theoretical and practical implications provide five priority indicators for the success of a sustainable organization.

The Validity of Autoregressive Integrated Moving Average Approach to Forecast the Spread of COVID-19 Pandemic in Africa

Africa’s first COVID-19 case was recorded in Egypt on February 14, 2020. Although it is not as expected by the World Health Organization (WHO) and other international organizations, currently a large number of Africans are getting infected by the virus. In this work, we studied the trend of the COVID-19 outbreak generally in Africa as a continent and in the five African regions separately. The study also investigated the validity of the ARIMA approach to forecast the spread of COVID-19 in Africa. The data of daily confirmed new COVID-19 cases from February 15 to October 16, 2020, were collected from the official website of Our World in Data to construct the autoregressive integrated moving average (ARIMA) model and to predict the trend of the daily confirmed cases through STATA 13 and EViews 9 software. The model used for our ARIMA estimation and prediction was (3, 1, 4) for Africa as a continent, ARIMA (3, 1, 3) for East Africa, ARIMA (2, 1, 3) for West Africa, ARIMA (2, 1, 3) for Central Africa, ARIMA (1, 1, 4) for North Africa, and ARIMA (4, 1, 5) for Southern Africa. Finally, the forecasted values were compared with the actual number of COVID-19 cases in the region. At the African level, the ARIMA model forecasted values and the actual data have similar signs with slightly different sizes, and there were some deviations at the subregional level. However, given the uncertain nature of the current COVID-19 pandemic, it is helpful to forecast the future trend of such pandemics by employing the ARIMA model.

Forecasting Shanghai Container Freight Index: A Deep-Learning-Based Model Experiment

With the increasing availability of large datasets and improvements in prediction algorithms, machine-learning-based techniques, particularly deep learning algorithms, are becoming increasingly popular. However, deep-learning algorithms have not been widely applied to predict container freight rates. In this paper, we compare a long short-term memory (LSTM) method and a seasonal autoregressive integrated moving average (SARIMA) method for forecasting the comprehensive and route-based Shanghai Containerized Freight Index (SCFI). The research findings indicate that the LSTM deep learning models outperformed SARIMA models in most of the datasets. For South America and the east coast of the U.S. routes, LSTM could reduce forecasting errors by as much as 85% compared to SARIMA. The SARIMA models performed better than LSTM in predicting freight movements on the west and east Japan routes. The study contributes to the literature in four ways. First, it presents insights for improving forecasting accuracy. Second, it helps relevant parties understand the trends of container freight markets for wiser decision-making. Third, it helps relevant stakeholders understand overall container shipping market trends. Lastly, it can help hedge against the volatility of freight rates.

Artificial Intelligence based Forecasting Techniques for the Covid-19 pandemic

The burial of bodies became a trend in the cause of ongoing pending (Novel Coronavirus), more than50 a million people all over the globe are adversely affected, hence the analysis and forecasting techniques are necessary to regain the human livelihood. The enlargement of technologies such as Artificial Intelligence, Machine Learning, Deep Learning, are en route into all the living aspects. Hence by using AI, ML, DL, Advanced technologies and existing models ARIMA, PROPHET, SVM, RNN, Faster Mask R-CNN, RESNET-50, and other techniques such as logarithmic scaling and exponential smoothing so on, the spread of VIRUS, the effect of countries economic growth, confirmed cases, fatality rate, recoveries are predicted to overcome the life threat due to SARS. Such that different predictive techniques are used to forecast. The advancement in the past algorithms to acquire accurate results are been introduced and described. © 2022 IEEE.

Data Analysis on Covid-19

The spread of COVID-19 across many parts of the world is an issue of concern for all government units of the world. India is also facing this very dark time and trying to control the spread. The source of data has been gathered from multiple sources and many other certified websites. The need is to predict the future accurately by telling when number will reach its peak and when it will decrease. Identities such as sex, longitudes and latitudes, age factor, etc. have been represented using R, data visualization techniques. Covid-19 analysis is the process of investigating number of people suffering from this on the basis of collection of data on growth rate through the use of networks. © 2021 IEEE.

Machine learning model of covid-19 forecasting in Ukraine based on the linear regression

Coronavirus or COVID-19 is a widespread pandemic that has affected almost all countries around the globe. The quantity of infected cases and deceased patients has been increasing at a fast pace globally. This virus not only is in charge of infecting billions of people but also affecting the economy of almost the whole world drastically. Thus, detailed studies are required to illustrating the following trend of the COVID-19 to develop proper short-term prediction models for forecasting the number of future cases. Generally, forecasting techniques are be inculcated in order to assist in designing better strategies and as well as making productive decisions. The forecasting techniques assess the situations of the past thereby enabling predictions about the situation in the future would be possible. Moreover, these predictions hopefully lead to preparation against potentially possible consequences and threats. It’s crucial to point out that Forecasting techniques play a vital role in drawing accurate predictions. In this research, we categorize forecasting techniques into different types, including stochastic theory mathematical models and data science/machine learning techniques. In this perspective, it is feasible to generate and develop strategic planning in the public health system to prohibit more deceased cases and managing infected cases. Here, some forecast models based on machine learning are introduced and comprising the Linear Regression model which is assessed for time series prediction of confirmed, deaths, and recovered cases in Ukraine and the globe. It turned out that the Linear Regression model is feasible to implement and reliable in illustrating the trend of COVID-19. © 2021 IEEE.

Indian COVID-19 time series prediction using Facebook's Prophet model

The entire world has been facing an unprecedented public health crisis due to Covid-19 pandemic for the last one year. Meanwhile, more than one million people across the world have already died;many more millions are under treatment. Some countries in Europe have begun to experience the second wave of the pandemic too. This has put the entire health infrastructure of countries under severe strain and has led to downward spiral in the economy. The most worrisome part is the uncertainty as to the spread or arrest of the pandemic. In such a scenario, robust forecasting methods are needed to enable health professionals and governments to make necessary preparation in accordance with the situation. Artificial Intelligence and Machine Learning techniques are useful tools not only for collection of accurate data but also for prediction. Studies show that Time Series Forecasting Techniques like Facebook's Prophet have shown promising results. In this paper, Time Series Techniques have been used to forecast the numbers of death, recovery and positive cases 60 days ahead. The experimental results obtained demonstrate that machine learning techniques can be beneficial in forecasting the behaviour of the pandemic.

The increased importance of competitor benchmarking as a strategic management tool during COVID-19 recovery

PurposeWhile all recoveries are good, some are better than others with regard to their speed and/or magnitude. Many revenue-related key performance indicators (KPIs), such as comparisons to budgets and forecasts that were designed pre-pandemic to assess a hotel's or destination's performance are no longer valid. Therefore, the primary purpose of this conceptual paper is to highlight the need to peg financial-related KPIs relative to competitors' performance during and following a radical market disruption. The secondary purpose of this paper is to summarize advances reported in the literature and in the industry related to competitor benchmarking and accurately identifying competitor sets.Design/methodology/approachThis conceptual paper synthesizes research from disparate sources to offer a series of recommendations to the industry regarding best practices for developing and monitoring revenue-related KPIs during pandemic recovery. Such KPIs will be different based upon hospitality or tourism sector but must be largely founded upon benchmarking off comparable operations.FindingsIndustry disruptions triggered by COVID-19 underscore the need (1) to increasingly utilize competitor-based revenue KPI benchmarks;(2) to have reliable competitor benchmarking data more readily available for use by hotels and destination marketing organizations (DMOs) and (3) for both hotels and DMOs to more accurately identify their competitive sets.Originality/valueThe recommendations offered in this paper are anchored with appropriate theories and empirical research;and as a consequence, offer guidance for the industry for KPI formulation during and following the pandemic.