A Forecasting Study of Covid-19 Epidemic: Turkey Case

The Coronavirus (Covid-19) is an infectious disease and has spread over the 170 countries. The pandemic brings new challenges to the research community. Many measures are taken by countries and developed vaccines limit the spread of pandemic. Globally, there have been more than 490 million confirmed case of Covid-19, and 6.1 million deaths reported World Health Organization as of April 4, 2022. Disease modelling has critical policy impact on Covid-19. Forecasting is one of the key purposes of epidemic modelling. It will not only help the governments but also, the medical practitioners to know the future trajectory of the spread, which might help them with the best possible treatments, precautionary measures and protections. This study makes a forecasting of Covid-19 for Turkey. Specifically, a multi-step forecasting model is proposed. Additionally, the effect of some measures taken against Covid-19 are analyzed. The study period covers 11 March 2020 - 16 March 2022 and number of confirmed cases is selected as indicator. A summary information is given about the course of the pandemic in Turkey and the fight against Covid-19. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG

Forecasting intermittent demand using the cox process

If a demand has infrequent demand occurrences and irregular demand sizes, then it is intermittent demand. Generally, intermittent demand appears at random, with many time periods having no demand. Owing to peculiar characteristics of intermittent demand, demand forecasting for intermittent demand is especially difficult. There are ad hoc methods developed for intermittent demand forecasting. Since Cox process has shown superior performance for intermittent demand forecasting, we studied forecasting intermittent demand using Cox process in this study. We develop a new method for estimating Cox process intensity which is called Reversed Leven and Segerstedt (RLS) method. Moreover, we propose a novel method which is a Wavelet Transform and Reversed Leven and Segerstedt conjunction model for intermittent demand forecasting using Cox process. Using real data set of 500 kinds of spare parts from an aviation sector company in Turkey, we show that our method produces more accurate forecasts than other intermittent demand forecasting methods using Cox process. The comparison approach has a lead time perspective which is based on lead time ahead demand forecast and lead time demand forecast errors. © 2018 Old City Publishing, Inc