The application of forecasting sales of services to increase business competitiveness

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.Web of Science1221059

Profitability analysis of urban mass transport lines using activity-based costing method: An evidence from the Czech Republic

The objective of this study is to present how could be Activity-Based Costing method used for the purpose of the profitability analysis of individual bus and trolleybus lines in an urban mass transport company which operates the land public transport inside the medium sized town in Czech Republic. Activity-Based Costing method had been used, in order to causal allocation of overhead costs to individual operations in order to measure the profitability of particular transport lines. The performed study showed the application process of the Activity-Based Costing and possible information outputs for an urban mass transport company as well as the limitations of the method use in the field of transportation services. The study also analyses the obstacles in effective data collection and processing which implementation team faced during the analysis. The primary limitation of the analysis, is similarly as in other studies, is the quality of the non-financial information which had to be obtained. Study discusses problems related with the fare system, which does not provide the information regarding the route taken by individual passenger. The study presents the how could be ABC method used for the decision making support in urban mass transport company and shows a real example of the ABC system information outputs. © 2016, Institut za Istrazivanja. All rights reserved

Hybrid demand forecasting models: pre-pandemic and pandemic use studies

Research background: In business practice and academic sphere, the question of which of the prognostic models is the most accurate is constantly present. The accuracy of models based on artificial intelligence and statistical models has long been discussed. By combining the advantages of both groups, hybrid models have emerged. These models show high accuracy. Moreover, the question remains whether data in a dynamically changing economy (for example, in a pandemic period) have changed the possibilities of using these models. The changing economy will contin-ue to be an important element in demand forecasting in the years to come. In business, where the concept of just in time already proves to be insufficient, it is necessary to open new research questions in the field of demand forecasting.Purpose of the article: The aim of the article is to apply hybrid models to bicycle sales e-shop data with a comparison of accuracy models in the pre-pandemic period and in the pandemic period. The paper examines the hypothesis that the pandemic period has changed the accuracy of hybrid models in comparison with statistical models and models based on artificial neural net-works.Models: In this study, hybrid models will be used, namely the Theta model and the new fore-castHybrid, compared to the statistical models ETS, ARIMA, and models based on artificial neural networks. They will be applied to the data of the e-shop with the cycle assortment in the period from 1.1. 2019 to 5.10 2021. Whereas the period will be divided into two parts, pre -pandemic, i.e. until 1 March 2020 and pandemic after that date. The accuracy evaluation will be based on the RMSE, MAE, and ACF1 indicators.Findings & value added: In this study, we have concluded that the prediction of the Hybrid model was the most accurate in both periods. The study can thus provide a scientific basis for any other dynamic changes that may occur in demand forecasting in the future. In other periods when there will be volatile demand, it is essential to choose models in which accuracy will decrease the least. Therefore, this study provides guidance for the use of methods in future periods as well. The stated results are likely to be valid even in an international comparison.Web of Science17372569

Demand forecasting: An alternative approach based on technical indicator Pbands

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.Web of Science1241094106

Demand forecasting: AI-based, statistical and hybrid models vs practice-based models - the case of SMEs and large enterprises

Demand forecasting is one of the biggest challenges of post-pandemic logistics. It appears that logistics management based on demand prediction can be a suitable alternative to the just-in-time concept. This study aims to identify the effectiveness of AI-based and statistical forecasting models versus practice-based models for SMEs and large enterprises in practice. The study compares the effectiveness of the practice-based Prophet model with the statistical forecasting models, models based on artificial intelligence, and hybrid models developed in the academic environment. Since most of the hybrid models, and the ones based on artificial intelligence, were developed within the last ten years, the study also answers the question of whether the new models have better accuracy than the older ones. The models are evaluated using a multicriteria approach with different weight settings for SMEs and large enterprises. The results show that the Prophet model has higher accuracy than the other models on most time series. At the same time, the Prophet model is slightly less computationally demanding than hybrid models and models based on artificial neural networks. On the other hand, the results of the multicriteria evaluation show that while statistical methods are more suitable for SMEs, the prophet forecasting method is very effective in the case of large enterprises with sufficient computing power and trained predictive analysts.Web of Science154623

Ověření a posouzení vhodnosti použití vybraných opčních strategií s opcemi na burzovní index

Prezenční154 - Katedra financíNeuveden

Vliv receptury a délky zrání na obsah polyaminů ve zrajících sýrech

The theoretical part of my thesis was focused on the characteristics of polyamines, their synthesis, degradation and effect in the organism. The practical part was involved on determining the content of polyamines (putrescine, spermine and spermidine) in ripening cheese without addition and with the addition of laccase. The high-performance liquid chromatography method was used for the analysis. Quantitatively, the most significant polyamine in all tested cheese variants was spermine (laccase in cheese grains 16.6 mg/kg > control 15.5 mg/kg > surface laccase 14.8 mg/kg; p laccase in cheese grains 2.9 mg/kg > control 2.4 mg/kg; p control 1.3 mg/kg > laccase in surface 0.7 mg/kg; p < 0.05). During storage (X; weeks), the total PA content (Y; mg/kg) in the control cheese sample without the addition of laccase increased according to the equations: y = 256.93 + 207.38x (R² = 0.8955; p = 0.0021 ), the amount of PA in the sample of cheese with laccase in the cheese grains also increased linearly according to equation: y = 196.05 + 228.22x (R² = 0.8226; p = 0.0024). The total PA content in the cheese sample with laccase on the surface increased up to 4 weeks and then decreased according to the equation: y = 629.1 + 621.45x - 59.539x2 (R² = 0.8984; p = 0.0163). The effect of laccase on the degradation of amines described in the literature has not been proven. However, this is a pilot experiment that investigates the effect of laccase on the content of polyamines in ripening cheeses. For a better understanding of this problematics, will be needed more extensive research

Testing EMA Indicator for the Currency Pair EUR / USD

The aim of this paper is to verify the effectiveness of EMA indicator according to selected time intervals. The underlying assumption is that, on longer timescales EMA is profitable and provides more relevant signals. The second objective of this paper is to test the signals of indicators in different months. It is believed that in September and January the number of trading signals on this indicator will increase. Testing will be done on the five-minute time frame. The test will be subjected to 65,000 rate values of the EUR / USD currency pair. Effectiveness of the analysis will be evaluated on the basis of digital (binary) option. Business strategy is based on EMA crossover indicator of current exchange rate. By the contribution there were confirmed hypotheses about more profitable signals when selecting a greater timeframe breadth of moving average. There was also confirmed an increased amount of signals in September, but not in January

Binary Options as a Modern Fenomenon of Financial Business

Binary options are a new instrument of the financial market. The aim of this paper is to analyze the use of binary options with trading and to illustrate this on the practical example of trades based on Bollinger bands indicator. Currency pair EUR/USD and 6912 time series values of this instrument will be put to analysis. The contribution will be evaluated 8 strategies based on Bollinger Bands. There will be used a backtesting method. From the results follows the most trades could have been realized with the use of Bollinger bands with a double deviation. This strategy, however, also showed the greatest percentage of failed trades. On the contrary the fewest transactions could have been carried out with Bollinger bands with a triple deviation and the MACD filter

Demand forecasting in Python: Deep learning model based on LSTM architecture versus statistical models

Demand forecasting for business practice is one of the biggest challenges of current business research. However, the discussion on the use of forecasting methods in business is still at the beginning. Forecasting methods are becoming more accurate. Accuracy is often the only criterion for forecasting. In the reality of business practice or management is also influenced by other factors such as runtime, computing demand, but also the knowledge of the manager. The goal of this article is to verify the possibilities demand forecasting using deep learning and statistical methods. Suitable methods are determined on based multi-criteria evaluation. Accuracy according to MSE and MAE, runtime and computing demand and knowledge requirements of the manager were chosen as the criteria. This study used univariate data from an e-commerce entity. It was realized 90-days and 365-days demand forecasting. Statistical methods Seasonal naive, TBATS, Facebook Prophet and SARIMA was used. These models will be compared with a deep learning model based on recurrent neural network with Long short-term memory (LSTM) layer architecture. The Python code used in all experiments and data is available on GitHub (https://github.com/mrnavrc/demand_forecasting). The results show that all selected methods surpassed the benchmark in their accuracy. However, the differences in the other criteria were large. Models based on deep learning have proven to be the worst on runtime and computing demand. Therefore, they cannot be recommended for business practice. As a best practice model has proven Prophet model developed at Facebook.Web of Science18814112