One-dimensional convolutional layers in a neural network for wind speed time series analysis

Data analysis using neural networks and deep machine learning is one of the current trends in scientific research in various fields. One of the scientific tasks of this direction is the study and prediction of time series using artificial intelligence. The article discusses the results of experiments on adding one-dimensional convolutional layers to a neural network within the framework of the task of classifying meteorological time series data – wind speed. The accuracy of the forecast is shown to increase due to the inclusion of one-dimensional convolutional layers in the model. The increase in accuracy on the test data set for the problem under consideration is about 9.5 %. Several variants of architectures for building a model with one-dimensional convolutional layers and evaluating the accuracy of their classification after machine learning are given. The results obtained allow us to conclude that the use of one-dimensional convolutional layers in the neural network architecture is effective for identifying and predicting a time series of meteorological parameters.