Time-Series Forecasting of Indoor Temperature Using Pre-trained Deep Neural Networks

2013 ◽  
pp. 451-458 ◽  
Author(s):  
Pablo Romeu ◽  
Francisco Zamora-Martínez ◽  
Paloma Botella-Rocamora ◽  
Juan Pardo
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Deep Neural Networks ◽  
Time Series Forecasting ◽  
Indoor Temperature

Modern technologies of deep learning for forecasting time series

2021 ◽  
pp. 95-105
Author(s):  
А.И. Сотников
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Time Series ◽  
Big Data ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
High Accuracy ◽  
Time Series Forecasting ◽  
Machine Learning Methods ◽  
Modern Technologies

Прогнозирование временных рядов стало очень интенсивной областью исследований, число которых в последние годы даже увеличивается. Глубокие нейронные сети доказали свою эффективность и достигают высокой точности во многих областях применения. По этим причинам в настоящее время они являются одним из наиболее широко используемых методов машинного обучения для решения проблем, связанных с большими данными. Time series forecasting has become a very intensive area of research, the number of which has even increased in recent years. Deep neural networks have been proven to be effective and achieve high accuracy in many applications. For these reasons, they are currently one of the most widely used machine learning methods for solving big data problems.

Time Series Forecasting of Cyanobacteria Blooms in the Crestuma Reservoir (Douro River, Portugal) Using Artificial Neural Networks

2006 ◽  
Vol 38 (2) ◽  
pp. 227-237 ◽  
Author(s):  
Luis Oliva Teles ◽  
Vitor Vasconcelos ◽  
Luis Oliva Teles ◽  
Elisa Pereira ◽  
Martin Saker ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Time Series Forecasting ◽  
Cyanobacteria Blooms ◽  
Artificial Neural ◽  
Douro River
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