Abstract
The article is devoted to the new method of preparation of time series data and its prediction made by neural networks. A detailed analysis of the methodology and comparison of the results with ARIMA method carried out. A full table of initial data and forecasting results for the export of goods and services for 2021 accompanies the article.
Highlights
Forecasting of time series of economic data is relevant and important for companies engaged in planning process in the various sectors of the economy
We describe the results of neural network training, and the results of applying the neural network model to the time series data
That the obtained model of the neural networks can be widely used for time series forecasting, together with the well-known methods [5, 6], like: Regression forecasting models; The autoregressive forecasting model (ARIMA, GARCH, ARDLM); Exponential smoothing models (ES); Model by maximum similarity sampling (MMSP); The model on the Markov chains (Markov chains); Model for classification and regression trees (CART); Model based on genetic algorithm (GA); Model support vector machine (SVM); Model based on transfer functions (TF); Fuzzy logic model (FL); Especially if it proved in practice that our method gives better speed of calculation and data preparation and acceptable accuracy of the results
Summary
Forecasting of time series of economic data is relevant and important for companies engaged in planning process in the various sectors of the economy. The availability of a fast and correct method of time series forecasting is usually the basis for building a modern, multi-parameter and proactive decision support system
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