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1	Validation and Investigation of the Four Aspects of Cycle Regression: A New Algorithm for Extracting Cycles Mehta, Mayur Ravishanker 12 1900 (has links) The cycle regression analysis algorithm is the most recent addition to a group of techniques developed to detect "hidden periodicities." This dissertation investigates four major aspects of the algorithm. The objectives of this research are 1. To develop an objective method of obtaining an initial estimate of the cycle period? the present procedure of obtaining this estimate involves considerable subjective judgment; 2. To validate the algorithm's success in extracting cycles from multi-cylical data; 3. To determine if a consistent relationship exists among the smallest amplitude, the error standard deviation, and the number of replications of a cycle contained in the data; 4. To investigate the behavior of the algorithm in the predictions of major drops. mathematical forecasting cycle regression analysis algorithm hidden periodicities Business cycles -- Forecasting. Forecasting -- Mathematical models. Time-series analysis.
2	Разработка модуля информационной системы предприятия на основе математической модели прогнозирования развития рынка E-COMMERCE по ключевым параметрам : магистерская диссертация / Development of an enterprise information system module based on a mathematical model for predicting the development of the E-COMMERCE market by key parameters Насекина, А. А., Nasekina, A. A. January 2022 (has links) The dissertation discusses the main methods of sales forecasting for a company with the E-COMMERCE line of business. The optimal model for predicting sales of B2C goods is found, and a method for updating the model using the index of purchasing activity in online stores. A software module developed based on a new mathematical model for forecasting sales for 2022 for the company BOXBERRY SOFT. / В диссертации рассмотрены основные методы прогнозирования продаж для компании с направлением деятельности E-COMMERCE. Найдена оптимальная модель для прогнозирования продаж товара B2C, а также предложен способ модернизации модели с помощью индекса покупательской активности в Интернет-магазинах. Разработан программный модуль на основе новой математической модели для прогноза продаж на 2022 год для компании ООО «БОКСБЕРРИ СОФТ». E-COMMERCE СЕЗОННАЯ КОМПОНЕНТА ИНТЕРНЕТ-МАГАЗИН MASTER'S THESIS MATHEMATICAL FORECASTING METHODS ELECTRONIC COMMERCE E-COMMERCE SEASONAL COMPONENT CONSUMER ACTIVITY INDEX ONLINE STORE

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Validation and Investigation of the Four Aspects of Cycle Regression: A New Algorithm for Extracting Cycles