Deep Learning for Big Data Time Series Forecasting Applied to Solar Power

Author(s):  
J. F. Torres ◽  
A. Troncoso ◽  
I. Koprinska ◽  
Z. Wang ◽  
F. Martínez-Álvarez
Author(s):  
А.И. Сотников

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.


2018 ◽  
Vol 25 (4) ◽  
pp. 335-348 ◽  
Author(s):  
J.F. Torres ◽  
A. Galicia ◽  
A. Troncoso ◽  
F. Martínez-Álvarez

2020 ◽  
Author(s):  
Pathikkumar Patel ◽  
Bhargav Lad ◽  
Jinan Fiaidhi

During the last few years, RNN models have been extensively used and they have proven to be better for sequence and text data. RNNs have achieved state-of-the-art performance levels in several applications such as text classification, sequence to sequence modelling and time series forecasting. In this article we will review different Machine Learning and Deep Learning based approaches for text data and look at the results obtained from these methods. This work also explores the use of transfer learning in NLP and how it affects the performance of models on a specific application of sentiment analysis.


Author(s):  
Mohammed Atef ◽  
Ahmed Khattab ◽  
Essam A. Agamy ◽  
Mohamed M. Khairy

Author(s):  
Imran Qureshi ◽  
Burhanuddin Mohammad ◽  
Mohammed Abdul Habeeb ◽  
Mohammed Ali Shaik

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