Analysis of Machine Learning Algorithms for Time Series Prediction

Due to the rapidly increasing prominence of Artificial Intelligence in the last decade and the advancements in technology such as processing power and data storage, there has been increased interest in applying machine learning algorithms to time series prediction problems. There are many machine learning algorithms that can be used for time series prediction problems but selecting an algorithm can be challenging due to algorithms not being suitable to all types of datasets. This research investigates and evaluates machine learning algorithms that can be used for time series prediction. Experiments were carried out using the Artificial Neural Network (ANN), Support Vector Regressor (SVR) and Long Short-Term Memory (LSTM) algorithms on eight datasets. An empirical analysis was carried out by applying each machine learning algorithm to the selected datasets. A critical comparison of the algorithm performance was carried out using the Mean Absolute Error (MAE), the Mean Squared Error (MSE), the Root Mean Squared Error (RMSE) and the Mean Absolute Scaled Error (MASE). The second experiment focused on evaluating the stability and robustness of the optimal models identified in the first experiment. The key dataset characteristics identified; were the dataset size, stationarity, trend and seasonality. It was found that the LSTM performed the best for majority of the datasets, due to the algorithm's ability to deal with sequential dependency. The performance of the ANN and SVR were similar for datasets with trend and seasonality, while the LSTM overall proved superior to the aforementioned algorithms. The LSTM outperformed the ANN and SVR due to its ability to handle temporal dependency. However, due to its stochastic nature, the LSTM and ANN algorithms can have poor stability and robustness. In this regard, the LSTM was found to be a more robust algorithm than the ANN and SVR

Оценка параметров структурных моделей временных рядов на основе принципов байесовской статистики

Осуществлен обзор публикаций по байесовской статистике, рассмотрено применение метода Байеса в различных отраслях, рассмотрена модель bsts. Разработано программное обеспечение с возможностью построения моделей для анализа и прогнозирования метеорологических величин, спроектирована обобщенная функциональная структура программного комплекса. Разработан веб-интерфейс созданного метода прогнозирования временных рядов с использованием пакета shiny языка R. На основе пакета bssm языка R выполнены эксперименты для прогнозирования пропущенных данных. На основе пакета bsts языка R проведён программный эксперимент для создания и оценивания bsts-модели.A review of publications on Bayesian statistics has been carried out, the application of the Bayesian method in various industries has been considered, and the bsts model has been considered. Software has been developed with the ability to build models for the analysis and forecasting of meteorological values, and a generalized functional structure of the software package has been designed. A web interface for the created method for time series forecasting was developed using the shiny package of the R language. Based on the R bssm package, experiments were performed to predict missing data. Based on the bsts package of the R language, a software experiment was carried out to create and evaluate the bsts model