The value of probabilistic forecasting in emergency medical resource planning under uncertainty

PurposeMost epidemic transmission forecasting methods can only provide deterministic outputs. This study aims to show that probabilistic forecasting, in contrast, is suitable for stochastic demand modeling and emergency medical resource planning under uncertainty.Design/methodology/approachTwo probabilistic forecasting methods, i.e. quantile regression convolutional neural network and kernel density estimation, are combined to provide the conditional quantiles and conditional densities of infected populations. The value of probabilistic forecasting in improving decision performances and controlling decision risks is investigated by an empirical study on the emergency medical resource planning for the COVID-19 pandemic.FindingsThe managerial implications obtained from the empirical results include (1) the optimization models using the conditional quantile or the point forecasting result obtain better results than those using the conditional density; (2) for sufficient resources, decision-makers' risk preferences can be incorporated to make tradeoffs between the possible surpluses and shortages of resources in the emergency medical resource planning at different quantile levels; and (3) for scarce resources, the differences in emergency medical resource planning at different quantile levels greatly decrease or disappear because of the existing of forecasting errors and supply quantity constraints.Originality/valueVery few studies concern probabilistic epidemic transmission forecasting methods, and this is the first attempt to incorporate deep learning methods into a two-phase framework for data-driven emergency medical resource planning under uncertainty. Moreover, the findings from the empirical results are valuable to select a suitable forecasting method and design an efficient emergency medical resource plan.

An AI-Enabled Predictive Analytics Dashboard for Acute Neurosurgical Referrals

Healthcare dashboards make key information about service and clinical outcomes available to staff in an easy-to-understand format. Most dashboards are limited to providing insights based on group-level inference, rather than individual prediction. Here, we evaluate a dashboard which could analyze and forecast acute neurosurgical referrals based on 10,033 referrals made to a large volume tertiary neurosciences center in central London, U.K., from the start of the Covid-19 pandemic lockdown period until October 2021. As anticipated, referral volumes significantly increased in this period, largely due to an increase in spinal referrals. Applying a range of validated time-series forecasting methods, we found that referrals were projected to increase beyond this time-point. Using a mixed-methods approach, we determined that the dashboard was usable, feasible, and acceptable to key stakeholders. Dashboards provide an effective way of visualizing acute surgical referral data and for predicting future volume without the need for data-science expertise.

Peramalan Penjualan Olein Curah di Perusahaan Pengolahan Kelapa Sawit Menggunakan Double Moving Average

Bulk olein is one of the products produced by Palm Oil Processing Company. Bulk cooking oil controls 75 percent of the production market share in Indonesia and about 77.5 percent of households in Indonesia use bulk cooking oil because the price is cheaper than packaged cooking oil. Demand for olein in the future is predicted to be continued to increase, so it is necessary to estimate future sales so that production activities become more effective and efficient. The method used in this study is the double moving average (DMA), which is one of the forecasting methods with data that has a trend. The calculation will be done by comparing the result of 3 moving, 4 moving and 5 moving. Forecasting error is calculated using mean absolute percentage error (MAPE). The calculation results show that the average MAPE from DMA with 5 moving has the smallest value. To verify these results, an analysis of the processed data was carried out, namely looking for data with the furthest distance from the linear line, namely t3 data and t7 data. The data is omitted in data processing and then the MAPE error value is recalculated. The results obtained are that DMA with 3 moving results have the smallest error, which is 11.863 percent. For this reason, the chosen forecasting calculation is a double moving average with 3 moving.

PERBANDINGAN METODE EXPONENTIAL SMOOTHING DAN ARIMA PADA PERAMALAN GARIS KEMISKINAN PROVINSI JAWA TENGAH

Forecasting is a technique that uses past data or historical data to determine something in the future. Forecasting methods with time series models consist of several methods, such as Double Exponential Smoothing (Holt method) and ARIMA. DES (Holt method) is a method that is used to predict time series data that has a trend pattern. ARIMA model combines AR and MA models with differencing order d. The poverty line is calculated by finding the total cost of all the essential resources that an average human adult consumes in one year. The lack of poverty reduction in an area is the lack of information about poverty. The selection of the forecasting method was made by considering several things. The Exponential Smoothing method was chosen because this method was able to predict time series financial data well and revise prediction errors. While the ARIMA method is better for short-term prediction, it can predict values that are difficult to explain by economic theory and are efficient in predicting time series financial data. There is still little research on comparing time series data for forecasting methods. Researchers are interested in comparing the Exponential Smoothing and ARIMA methods in implementing poverty line forecasting in Central Java. The two methods are compared by determining the best method for forecasting the poverty line in Central Java. The best forecasting method can be seen from the MAPE value of each method

A GIS-based AHP Method for Groundwater Potential Zone Assessment: A Review

Scientific and academic researches and studies trying to present a multi-range of techniques and methods focusing on groundwater pollution, potentials, assessment, and prediction, Groundwater is the most important resource of fresh water now and many researchers trying to cover all about this resource to get sustainable development. This review aims to create an overview of groundwater analysis and forecasting methods. The study is based on the need to select and group research papers into best-defined methodological categories. The article gives an overview of recent advancements in groundwater potential zone analysis approaches, as well as ongoing research objectives based on that overview. This review has overviewed papers and researches been published last decade 2010 -2020 have been done depending on the data sources from the global online database, which could obtain many papers and research studying the groundwater potential zones and other aspects related to groundwater. The aim of reviewing multiple types of research and papers on determining groundwater potential zones by applying the best techniques and selecting the most suitable factors that affect groundwater potential zones.

APPLICATION OF THE HOLT-WINTERS METHOD FOR FORECASTION HOSPITAL ADMISSIONS IN CITIES

The current situation of coronavirus infection necessitates the use of models and prediction methods for decision- making in a rapidly changing situation in large cities and towns. Due to the dynamics of the processes, it is necessary to use different models and forecasting methods for the development of the situation. The city of St. Petersburg is the object of study and an analysis of hospitalisation of patients is performed. St. Petersburg was chosen as the subject of the research. The presented research was carried out within the framework of the work "Mathematical modelling of logistic systems in medicine" during the performance of diploma projects on the assignment of St. Petersburg executive authorities in 2021. In the course of the study analytical data are collected, the city districts where the situation with transportation of patients to medical organizations is likely to be difficult to predict are identified, and the methodology of forecasting hospitalization of patients by ambulance cars is considered. In solving the problem of predicting the situation, forecasting methods in the class of polynomial models are used and the effectiveness of using the Holt-Winters method is justified.

A study of univariate forecasting methods for crude oil price

PurposeThis paper aims to compare different univariate forecasting methods to provide a more accurate short-term forecasting model on the crude oil price for rendering a reference to manages.Design/methodology/approachSix different univariate methods, namely the classical decomposition model, the trigonometric regression model, the regression model with seasonal dummy variables, the grey forecast, the hybrid grey model and the seasonal autoregressive integrated moving average (SARIMA), have been used.FindingsThe authors found that the grey forecast is a reliable forecasting method for crude oil prices.Originality/valueThe contribution of this research study is using a small size of data and comparing the forecasting results of the six univariate methods. Three commonly used evaluation criteria, mean absolute error (MAE), root mean squared error (RMSE) and mean absolute percent error (MAPE), were adopted to evaluate the model performance. The outcome of this work can help predict the crude oil price.

Analysis and Application of Grey-Markov Chain Model in Tax Forecasting

Tax data is a typical time series data, which is subject to the interaction and influence of economic and political factors and has dynamic and highly nonlinear characteristics. The key to correct tax forecasting is the choice of forecasting algorithm. Traditional tax forecasting methods, such as factor scoring method, factor regression method, and system adjustment method, have a certain guiding role in actual work, but there are still many shortcomings, such as the limitation from the distribution and size of sample data and difficulty of grasping the nonlinear phenomena in economic system. Grey-Markov chain model formed by the combination of grey forecasting and Markov chain forecasting can not only reveal the general developmental trend of time series data, but also predict their state change patterns. Based on the summary and analysis of previous research works, this paper expounds the current research status and significance of tax forecasting, elaborates the development background, current status, and future challenges of the Grey-Markov chain model, introduces the basic principles of grey forecasting model and Markov chain model, constructs the Grey-Markov chain model, analyzes the model’s residual error and posteriori error tests, conducts the analysis of Grey-Markov chain model, performs grey forecasting model construction and its state division, implements the calculation of transition probability matrix and the determination of tax forecasting value, discusses the application of the Grey-Markov chain model in tax forecasting, and finally carries out a simulation experiment and its result analysis. The study results show that, compared with separate grey forecasting, Markov chain forecasting, and other commonly used time series forecasting methods, the Grey-Markov chain model increases the accuracy of tax forecasts by an average of 2.3–13.1%. This indicates that the combinative forecasting of Grey-Markov chain model can make full use of the information provided by time series data for tax analysis and forecasting. It can not only avoid the influence of economic, political, and human subjective factors, but also have simple calculations, higher accuracy, and stronger practicality. The study results of this paper provide a reference for further researches on the analysis and application of Grey-Markov chain model in tax forecasting.

Demand Forecasting Method Proposal for a Packaging Industry at the Manaus Industrial Hub

Every year, many companies hold meetings with their experts to prospect sales for the following year. Projections are often based on the team's experience and are subject to bias and errors. This article seeks to propose a statistical method of forecasting demand for a packaging company located in the Industrial Pole of Manaus, comparing performance metrics from different analyses. The results suggested that in general, the predictions had a good performance, but in certain cases, the need for a more human vision was also felt in order to ponder certain points. Therefore, the combination of quantitative and qualitative demand forecasting methods is essential.