The emergence of new suppliers and energy resources has reshaped the energy market in terms of contractual structures and pricing systems. The market shifts were accelerated in response to the latest Russian-Ukraine crisis, impacting natural gas supply chains from financing projects to contracting volumes. The increased demand for liquified natural gas volumes intensified the need to switch from long-term oil-indexed contracts to short-term gas-indexed contracts. Those shifts were anticipated to influence the selling strategies for the expected added 49 MTPA of Qatari LNG, wherein increasing the share of spot selling would be reflected in higher economic performance. This study used forecasted prices to investigate potential Qatari LNG selling strategies. Initially, projections of the most dominant pricing systems used for pricing Qatari LNG (i.e., brent, Henry Hub, Title Transfer Facility, and Japan Korea Marker) were estimated between 2023 and 2040. While Qatar has been relying on long-term oil-indexed contracts, the second step estimated annual LNG revenues under different combinations of selling strategies (i.e., long-term and spot sales). Finally, the influence of varying brent slopes on the estimated revenues was measured. Due to data limitations and non-stationarity, the double exponential smoothing model was selected among the different tested models. Considering current market dynamics, forecasts of the double exponential smoothing model showed an upward price trend until 2040. An annual average increase of 1.24% for the studied pricing systems was reported. Reducing the share of long-term brent-indexed contracts to 70% and dedicating the remaining 30% of volumes to spot sales yielded the highest premiums for revenue estimates. An average annual revenue of $62 bn was reported for the 70/30 strategy, around 6% higher than the 100% brent-indexed contracts strategy. The findings revealed that diversifying the selling approach and introducing spot sales can enhance revenues. From the buyers\' perspective, the outcomes support policymakers in understanding the implications of escalated prices driven by a lack of liquidity investments.

In recent times, many investigators have delved into plastic waste (PW) research, both locally and internationally. Many of these studies have focused on problems related to land-based and marine-based PW management with its attendant impact on public health and the ecosystem. Hitherto, there have been little or no studies on forecasting PW quantities in developing countries (DCs). The key objective of this study is to provide a forecast on PW generation in the city of Johannesburg (CoJ), South Africa over the next three decades. The data used for the forecasting were historical data obtained from Statistics South Africa (StatsSA). For effective prediction and comparison, three-time series models were employed in this study. They include exponential smoothing (ETS), Artificial Neural Network (ANN), and the Gaussian Process Regression (GPR). The exponential kernel GPR model performed best on the overall plastic prediction with a determination coefficient (R2) of 0.96, however, on individual PW estimation, ANN was better with an overall R2 of 0.93. From the result, it is predicted that between 2021 and 2050, the total PW generated in CoJ is forecasted to be around 6.7 megatonnes with an average of 0.22 megatonnes/year. In addition, the estimated plastic composition is 17,910 tonnes PS per year; 13,433 tonnes PP per year; 59,440 tonnes HDPE per year; 4478 tonnes PVC per year; 85,074 tonnes PET per year; 34,590 tonnes LDPE per year and 8955 tonnes other PWs per year.

Across the globe, governments are developing policies and strategies to reduce carbon emissions to address climate change. Monitoring the impact of governments\' carbon reduction policies can significantly enhance our ability to combat climate change and meet emissions reduction targets. One promising area in this regard is the role of artificial intelligence (AI) in carbon reduction policy and strategy monitoring. While researchers have explored applications of AI on data from various sources, including sensors, satellites, and social media, to identify areas for carbon emissions reduction, AI applications in tracking the effect of governments\' carbon reduction plans have been limited. This study presents an AI framework based on long short-term memory (LSTM) and statistical process control (SPC) for the monitoring of variations in carbon emissions, using UK annual CO2 emission (per capita) data, covering a period between 1750 and 2021. This paper used LSTM to develop a surrogate model for the UK\'s carbon emissions characteristics and behaviours. As observed in our experiments, LSTM has better predictive abilities than ARIMA, Exponential Smoothing and feedforward artificial neural networks (ANN) in predicting CO2 emissions on a yearly prediction horizon. Using the deviation of the recorded emission data from the surrogate process, the variations and trends in these behaviours are then analysed using SPC, specifically Shewhart individual/moving range control charts. The result shows several assignable variations between the mid-1990s and 2021, which correlate with some notable UK government commitments to lower carbon emissions within this period. The framework presented in this paper can help identify periods of significant deviations from a country\'s normal CO2 emissions, which can potentially result from the government\'s carbon reduction policies or activities that can alter the amount of CO2 emissions.

In this paper, we propose a new short-term load forecasting (STLF) model based on contextually enhanced hybrid and hierarchical architecture combining exponential smoothing (ES) and a recurrent neural network (RNN). The model is composed of two simultaneously trained tracks: the context track and the main track. The context track introduces additional information to the main track. It is extracted from representative series and dynamically modulated to adjust to the individual series forecasted by the main track. The RNN architecture consists of multiple recurrent layers stacked with hierarchical dilations and equipped with recently proposed attentive dilated recurrent cells. These cells enable the model to capture short-term, long-term and seasonal dependencies across time series as well as to weight dynamically the input information. The model produces both point forecasts and predictive intervals. The experimental part of the work performed on 35 forecasting problems shows that the proposed model outperforms in terms of accuracy its predecessor as well as standard statistical models and state-of-the-art machine learning models.

UNASSIGNED: Syphilis remains a major public health concern in China. We aimed to construct an optimum model to forecast syphilis epidemic trends and provide effective precautionary measures for prevention and control.
UNASSIGNED: Data on the incidence of syphilis between 1982 and 2020 were obtained from the China Health Statistics Yearbook. An exponential smoothing model (ES model) and a BP neural network model were constructed, and on this basis, the ES-BP combination model was created. The prediction performance was assessed to compare the MAE (Mean Absolute Error), MSE (Mean Squared Error), MAPE (Mean Absolute Percentage Error), and RMSE (Root Mean Square Error).
UNASSIGNED: The optimum ES model was Brown\'s linear trend model, which had the lowest MAE and MAPE values, and its residual was a white noise sequence (P=0.359). The optimum BP neural network model had three layers with the number of nodes in the input, hidden, and output layers set to 5, 11, and 1, and the mean values of MAE, MSE, and RMSE by five-fold cross-validation were 1.519, 6.894, and 1.969, respectively. The ES-BP combination model had three layers, with model nodes 1, 4, and 1. The lowest mean values of MAE, MSE, and RMSE obtained by five-fold cross-validation were 1.265, 5.739, and 2.105, respectively.
UNASSIGNED: The ES, BP neural network, and ES-BP combination models can be used to predict syphilis incidence, but the prediction performance of the ES-BP combination model is better than that of a basic ES model and a basic BP neural network model.

The number of deaths due to road accident is increasing day by day and has become an alarming global problem over the decades. India, with her rising motorization is no stranger to this global catastrophe. In this paper two relatively simple yet powerful and versatile techniques for forecasting time series data, autoregressive integrated moving average method (ARIMA) and exponential smoothing method are used to forecast the number of deaths due to road accidents in India from the year 2022-2031. The results based on the two methods are compared and it is found that they are in sync with each other and pre-existing literature. Furthermore, this is a unique attempt to use two time series analysis techniques on the same data and carry out a comparative analysis. The data was collected from the annual report of Ministry of Road Transport and Highways, India (2020) and Accidental Deaths & Suicides in India (ADSI) Report of National Crime Record Bureau (2021). After examining all the probable models, it is observed that ARIMA (2, 2, 2) model and exponential smoothing (M, A, N) model are suitable for the given data. Amongst the two, ARIMA (2, 2, 2) model has a lower AIC and BIC value. Thus, this comes out to be the best model as per our model selection criterion. Further, the study also reveals an upward trend of number of road accidental deaths for the upcoming 10 years in India.

Forecasting is an attractive topic in every field of study because no one knows the exact nature of the underlying phenomena, but it can be guessed using mathematical functions. As the world progresses towards technology and betterment, algorithms are updated to understand the nature of ongoing phenomena. Machine learning (ML) algorithms are an updated phenomenon used in every task aspect. Real exchange rate data is assumed to be one of the significant components of the business market, which plays a pivotal role in learning market trends. In this work, machine learning models, i.e., the Multi-layer perceptron model (MLP), Extreme learning machine (ELM) model and classical time series models are used, Autoregressive integrated moving average (ARIMA) and Exponential Smoothing (ES) model to model and predict the real exchange rate data set (REER). The data under consideration is from January 2019 to June 2022 and comprises 864 observations. This study split the data set into training and testing and applied all stated models. This study selects a model that meets the Key Performance Indicators (KPI) criteria. This model was selected as the best candidate model to predict the behaviour of the real exchange rate data set.

Monkeypox is a critical public health emergency with international implications. Few confirmed monkeypox cases had previously been reported outside endemic countries. However, since May 2022, the number of monkeypox infections has increased exponentially in non-endemic countries, especially in North America and Europe. The objective of this study was to develop optimal models for predicting daily cumulative confirmed monkeypox cases to help improve public health strategies. Autoregressive integrated moving average (ARIMA), exponential smoothing, long short-term memory (LSTM) and GM (1, 1) models were employed to fit the cumulative cases in the world, the USA, Spain, Germany, the UK and France. Performance was evaluated by minimum mean absolute percentage error (MAPE), among other metrics. The ARIMA (2, 2, 1) model performed best on the global monkeypox dataset, with a MAPE value of 0.040, while ARIMA (2, 2, 3) performed the best on the USA and French datasets, with MAPE values of 0.164 and 0.043, respectively. The exponential smoothing model showed superior performance on the Spanish, German and UK datasets, with MAPE values of 0.043, 0.015 and 0.021, respectively. In conclusion, an appropriate model should be selected according to the local epidemic characteristics, which is crucial for monitoring the monkeypox epidemic. Monkeypox epidemics remain severe, especially in North America and Europe, e.g. in the USA and Spain. The development of a comprehensive, evidence-based scientific programme at all levels is critical to controlling the spread of monkeypox infection.

The Internet of Vehicles (IoV) is an interactive network providing intelligent traffic management, intelligent dynamic information service, and intelligent vehicle control to running vehicles. One of the main problems in the IoV is the reluctance of vehicles to share local data resulting in the cloud server not being able to acquire a sufficient amount of data to build accurate machine learning (ML) models. In addition, communication efficiency and ML model accuracy in the IoV are affected by noise data caused by violent shaking and obscuration of in-vehicle cameras. Therefore we propose a new Outlier Detection and Exponential Smoothing federated learning (OES-Fed) framework to overcome these problems. More specifically, we filter the noise data of the local ML model in the IoV from the current perspective and historical perspective. The noise data filtering is implemented by combining data outlier, K-means, Kalman filter and exponential smoothing algorithms. The experimental results of the three datasets show that the OES-Fed framework proposed in this article achieved higher accuracy, lower loss, and better area under the curve (AUC). The OES-Fed framework we propose can better filter noise data, providing an important domain reference for starting field of federated learning in the IoV.

Forecasting is a crucial step in almost all scientific research and is essential in many areas of industrial, commercial, clinical and economic activity. There are many forecasting methods in the literature; but exponential smoothing stands out due to its simplicity and accuracy. Despite the facts that exponential smoothing is widely used and has been in the literature for a long time, it suffers from some problems that potentially affect the model\'s forecast accuracy. An alternative forecasting framework, called Ata, was recently proposed to overcome these problems and to provide improved forecasts. In this study, the forecast accuracy of Ata and exponential smoothing will be compared among data sets with no or linear trend. The results of this study are obtained using simulated data sets with different sample sizes, variances. Forecast errors are compared within both short and long term forecasting horizons. The results show that the proposed approach outperforms exponential smoothing for both types of time series data when forecasting the near and distant future. The methods are implemented on the U.S. annualized monthly interest rates for services data and their forecasting performance are also compared for this data set.