Definition of neural network model for time series prediction

Abstract

This article is devoted to solving the issue of forecasting time series in the context of the functionality of project management systems, using the computational resources of neural networks. The article contains the results of analysis of neural network models and conducted experimental studies of the quality of their work, the neural network configuration was selected and adjusted, which allows predicting project execution time with sufficient accuracy. In the process of determining the structure of the neural network for time series prediction, several variants of recurring neural networks and LTSM models with different number of layers and neurons in them were considered, with varying sets of input data. Testing of the quality of neural networks was carried out on a sample of more than 300 thousand tasks for 15 projects accumulated over 8 years. Analysis of the convergence of the data predicted by the neural networks and the real deadlines for completing the tasks made it possible to design the final architecture of the neural network to predict the time of execution of projects. The neural network was developed for further integration into the project management system. The developed neural network was trained and tested. The test results showed that the prediction error in this case was determined within 20 hours. Evaluation of the duration of the task is usually carried out in days, respectively, forecasting by the developed system is carried out up to the day, which can be considered a completely satisfactory result