Information system for forecasting the collection of payments in the post offices of the Russian Post using machine learning
Abstract
Information system for forecasting the collection of payments in the post offices of the Russian Post using machine learning
Incoming article date: 29.04.2023This article discusses the forecasting of the collection of payments in post offices, taking into account seasonality and the use of machine learning. An algorithm for constructing a calculation model has been developed, which provides an opportunity for analysts of the Russian Post to make a monthly forecast of the collection of payments for each UFPS (Federal Postal Administration), taking into account seasonality. This model allows you to identify deviations from the norm in matters related to the collection of payments and more accurately adjust the increase in tariffs for services. The SSA algorithm is considered, which consists of 4 steps: embedding, singular decomposition, grouping, diagonal averaging. This information system is implemented in the form of a website using a framework ASP.NET Core and libraries for machine learning ML.NET . Then the forecast is evaluated using various methods.
Keywords: mathematical modeling, seasonally adjusted forecasting, collection of payments, machine learning, neural network