Comparative Analysis of Neural Networking and Regression Models for Time Series Forecasting
Applicability of neural nets in time series forecasting has been considered and researched. For this, training of neural network on various time series with preliminary selection of optimal hyperparameters has been performed. Comparative analysis of received neural networking forecasting model with...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | Russian |
| Published: |
Ministry of Education of the Republic of Belarus, Establishment The Main Information and Analytical Center
2019-08-01
|
| Series: | Цифровая трансформация |
| Subjects: | |
| Online Access: | https://dt.bsuir.by/jour/article/view/174 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832557205003436032 |
|---|---|
| author | S. V. Sholtanyuk |
| author_facet | S. V. Sholtanyuk |
| author_sort | S. V. Sholtanyuk |
| collection | DOAJ |
| description | Applicability of neural nets in time series forecasting has been considered and researched. For this, training of neural network on various time series with preliminary selection of optimal hyperparameters has been performed. Comparative analysis of received neural networking forecasting model with linear regression has been performed. Conditions, affecting on accuracy and stability of results of the neural network, have been revealed. |
| format | Article |
| id | doaj-art-e8f5f4976be74e11bffd84007be872eb |
| institution | Kabale University |
| issn | 2522-9613 2524-2822 |
| language | Russian |
| publishDate | 2019-08-01 |
| publisher | Ministry of Education of the Republic of Belarus, Establishment The Main Information and Analytical Center |
| record_format | Article |
| series | Цифровая трансформация |
| spelling | doaj-art-e8f5f4976be74e11bffd84007be872eb2025-02-03T05:39:02ZrusMinistry of Education of the Republic of Belarus, Establishment The Main Information and Analytical CenterЦифровая трансформация2522-96132524-28222019-08-0102606810.38086/2522-9613-2019-2-60-6895Comparative Analysis of Neural Networking and Regression Models for Time Series ForecastingS. V. Sholtanyuk0Belarusian State UniversityApplicability of neural nets in time series forecasting has been considered and researched. For this, training of neural network on various time series with preliminary selection of optimal hyperparameters has been performed. Comparative analysis of received neural networking forecasting model with linear regression has been performed. Conditions, affecting on accuracy and stability of results of the neural network, have been revealed.https://dt.bsuir.by/jour/article/view/174neural networktraining of neural networkhyperparametersforecasting accuracy and stabilitymaelinear regressionautoregressionordinary least squares |
| spellingShingle | S. V. Sholtanyuk Comparative Analysis of Neural Networking and Regression Models for Time Series Forecasting Цифровая трансформация neural network training of neural network hyperparameters forecasting accuracy and stability mae linear regression autoregression ordinary least squares |
| title | Comparative Analysis of Neural Networking and Regression Models for Time Series Forecasting |
| title_full | Comparative Analysis of Neural Networking and Regression Models for Time Series Forecasting |
| title_fullStr | Comparative Analysis of Neural Networking and Regression Models for Time Series Forecasting |
| title_full_unstemmed | Comparative Analysis of Neural Networking and Regression Models for Time Series Forecasting |
| title_short | Comparative Analysis of Neural Networking and Regression Models for Time Series Forecasting |
| title_sort | comparative analysis of neural networking and regression models for time series forecasting |
| topic | neural network training of neural network hyperparameters forecasting accuracy and stability mae linear regression autoregression ordinary least squares |
| url | https://dt.bsuir.by/jour/article/view/174 |
| work_keys_str_mv | AT svsholtanyuk comparativeanalysisofneuralnetworkingandregressionmodelsfortimeseriesforecasting |