Non-stationary time series prediction using one-dimensional convolutional neural network models

Abstract

The main goal of non-stationary time series prediction is the construction, identification, configuration and verification of their models. The efficiency of using machine learning technologies for the analysis of non-stationary time series is shown due to their ability to model complex nonlinear dependencies in the behaviour of the time series from both depending on previous values and external factors, to analyse features, relationships and complex interactions. The features of time series prediction using a one-dimensional convolutional neural network are discussed. The features of the architecture and the training process when using a one-dimensional convolutional neural network are considered on the example of solving the problems to predict sales and build a forecast of company stock prices. To improve the quality of the prediction, the initial time series were pre-processed by the moving average method in the window. Computer modelling of the predicting problem using the one-dimensional convolutional neural network was performed in the Python programming language. The sales prediction using the proposed one-dimensional convolutional neural network model predicted volume sale of cars and commercial vehicles in Vietnam from two thousand and eleven to two thousand and eighteen. The one-dimensional convolutional neural network model has given a high accuracy of prediction with seasonal trend data. In stock prices prediction, another architecture of one-dimensional convolutional neural network model was launched, which corresponds to non-stationary data with large lengths of data series with small intervals between minutes, such as stock trading statistics per minute. In this project, data is taken from Amazon Nasdaq one hundred for forty thousand five hundred and sixty data points. The data is divided into training and test sets. The test suite is used to verify the actual performance of the model. It is shown that the model of a one-dimensional convolutional neural network gives good results in the presence of both seasonal and trend components of the time series with large data sizes.