The accurate forecasting of business variables is a key element for a company's competitiveness which is becoming increasing necessary in this globalized and digitalized environment. Companies are responding to this need by intensifying accuracy requirements for forecasting economic variables. The objective of this article is to verify the correctness of the models predicting revenue in the service sector against 6 precision criteria to determine whether the use of certain criteria may lead to the adoption of particular models to improve competitive forecasting. This article seeks to determine the best accuracy predictors in 32 service areas broken down by NACE. Exponential smoothing models, ARIMA models, BATS models and artificial neural network models were selected for the assessment. Six criteria were chosen to measure accuracy using a group of scale-dependent errors and scaled errors. Services for which the result was ambiguous were subject to complete forecasting, both ex-post and ex-ante. Based on the analysis, the main result of the article is that only two types of services do not achieve the same accuracy results when using other measure criteria. It can therefore be said that for 93.75% of services, an assessment according to one precision parameter would suffice. Thus, a model's competitiveness is not affected by the choice of accuracy.
Competitiveness is an important factor in a company's ability to achieve success, and proper forecasting can be a fundamental source of competitive advantage for an enterprise. The aim of this study is to show the possibility of using technical analysis indicators in forecasting prices in the food industry in comparison with classical methods, namely exponential smoothing. In the food industry, competitiveness is also a key element of business. Competitiveness, however, requires not only a thorough historical analysis not only of but also forecasting. Forecasting methods are very complex and are often prevented from wider application to increase competitiveness. The indicators of technical analysis meet the criteria of simplicity and can therefore be a good way to increase competitiveness through proper forecasting. In this manuscript, the use of simple forecasting tools is confirmed for the period of 2009-2018. The analysis was completed using data on the main raw materials of the food industry, namely wheat food, wheat forage, malting barley, milk, apples and potatoes, for which monthly data from January 2009 to February 2018 was collected. The data file has been analyzed and modified, with an analysis of indicators based on rolling averages selected. The indicators were compared using exponential smoothing forecasting. Accuracy RMSE and MAPE criteria were selected. The results show that, while the use of indicators as a default setting is inappropriate in business economics, their accuracy is not as strong as the accuracy provided by exponential smoothing. In the following section, the models were optimized. With these optimized parameters, technical indicators seem to be an appropriate tool.
Research background: Demand forecasting helps companies to anticipate purchases and plan the delivery or production. In order to face this complex problem, many statistical methods, artificial intelligence-based methods, and hybrid methods are currently being developed. However, all these methods have similar problematic issues, including the complexity, long computing time, and the need for high computing performance of the IT infrastructure. Purpose of the article: This study aims to verify and evaluate the possibility of using Google Trends data for poetry book demand forecasting and compare the results of the application of the statistical methods, neural networks, and a hybrid model versus the alternative possibility of using technical analysis methods to achieve immediate and accessible forecasting. Specifically, it aims to verify the possibility of immediate demand forecasting based on an alternative approach using Pbands technical indicator for poetry books in the European Quartet countries. Methods: The study performs the demand forecasting based on the technical analysis of the Google Trends data search in case of the keyword poetry in the European Quartet countries by several statistical methods, including the commonly used ETS statistical methods, ARIMA method, ARFIMA method, BATS method based on the combination of the Cox-Box transformation model and ARMA, artificial neural networks, the Theta model, a hybrid model, and an alternative approach of forecasting using Pbands indicator. The study uses MAPE and RMSE approaches to measure the accuracy. Findings & value added: Although most currently available demand prediction models are either slow or complex, the entrepreneurial practice requires fast, simple, and accurate ones. The study results show that the alternative Pbands approach is easily applicable and can predict short-term demand changes. Due to its simplicity, the Pbands method is suitable and convenient to monitor short-term data describing the demand. Demand prediction methods based on technical indicators represent a new approach for demand forecasting. The application of these technical indicators could be a further forecasting models research direction. The future of theoretical research in forecasting should be devoted mainly to simplifying and speeding up. Creating an automated model based on primary data parameters and easily interpretable results is a challenge for further research.
The purpose of the study is to evaluate the use of quantitative methods in companies and determine the reasons for the non-use of quantitative methods. First, the context of dependence on individual characteristics of the company, such as size, amount of foreign share, and business sector, is analysed. Subsequently, the methods companies use, and those for which companies require scientific support and research are identified. The study is carried out in companies in the Czech Republic and is based on an electronic survey. Methods of statistical testing of dependencies are used for evaluation. The inputs showed that the size of the company is the most dependent on the use of quantitative methods in companies. From the results of the question on the reasons for not using quantitative methods in business practice, the most significant number of respondents answered that the methods are too academic and often their use in business practice is unrealistic due to their complexity. It is the trend of academic institutions and scientific societies to develop quantitative evaluation software with simple input and interpretation. They identified the forecasting of business variables as the method with the greatest scientific potential. The following are simulation methods and logistics management methods. The article may provide a central theme for future science and research development in business.
There are many methods of forecasting, often based on the specific conditions of the given time series which are frequently the result of research in scientific centres and universities. Nevertheless, there are also models that were created by scientists in a particular company, examples may be Google or Facebook. The latter one has developed one of the latest Prophet forecasting models published in 2017 by Taylor & Letham. This model is completely new and so it is appropriate to subject it to further research, which is the topic of this article. To accomplish this research objective, the aim of this work is to identify seasonal trends in revenue development in a selected e-commerce segment based on the assessment of the applicability of the Facebook Prophet forecasting tool. To accomplish this goal, the Python Prophet is decomposed with a subsequent two-year forecast. Accuracy of this model is measured by RMSA and coverage. The e-commerce subject selected is active primarily in the field of sales of professional outdoor supplies and organizing outdoor educational courses, seminars and competitions. It is clear from the prediction that the e-commerce entity shows a strong sales period with the beginning of the spring season and then, due to the summer, decline, until the pre-Christmas period. The subject has little growth potential and a new impetus is needed to increase sales and thus restore the growth trend. It has been confirmed that Prophet is a suitable tool for debugging seasonal tendencies.
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