Прогнозування часових рядів методом LSTM

dc.contributor.advisor	Щестюк, Наталія
dc.contributor.author	Поляков, Михайло
dc.date.accessioned	2022-01-17T21:27:27Z
dc.date.available	2022-01-17T21:27:27Z
dc.date.issued	2021
dc.description.abstract	Time-series forecasting is a complex problem. Forecasting includes using models to fit historical information and using it to predict future data points. There are multiple methods of forecasting time-series data, including autoregressive models, like ARIMA, using gradient boosting like XGBoost, and more recently, using neural networks. The different techniques might be better for different types of data, and most of the time, only comparison between models can show the best model. We would try creating an LSTM based neural network with a multi-step multiple output strategy in this work. The completed model is tested on two different datasets in nature, power usage, and stock market prices.	uk_UA
dc.identifier.uri	https://ekmair.ukma.edu.ua/handle/123456789/22286
dc.language.iso	en	uk_UA
dc.status	first published	uk_UA
dc.subject	Time-series forecasting strategies	uk_UA
dc.subject	LSTM	uk_UA
dc.subject	магістерська робота	uk_UA
dc.title	Прогнозування часових рядів методом LSTM	uk_UA
dc.title.alternative	Time-series forecasting using LSTM	uk_UA
dc.type	Other	uk_UA

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