An Approximation to Deep Learning Touristic-Related Time Series Forecasting

2018 ◽  
pp. 448-456
Author(s):  
Daniel Trujillo Viedma ◽  
Antonio Jesús Rivera Rivas ◽  
Francisco Charte Ojeda ◽  
María José del Jesus Díaz
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Forecasting ◽  
Related Time Series

scholarly journals Temporal Convolutional Networks Applied to Energy-Related Time Series Forecasting

2020 ◽  
Vol 10 (7) ◽  
pp. 2322 ◽  
Author(s):  
Pedro Lara-Benítez ◽  
Manuel Carranza-García ◽  
José M. Luna-Romera ◽  
José C. Riquelme
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Energy Demand ◽  
Time Series Data ◽  
Demand Forecasting ◽  
Time Series Forecasting ◽  
Series Data ◽  
Recurrent Networks ◽  
Convolutional Networks ◽  
Related Time Series

Modern energy systems collect high volumes of data that can provide valuable information about energy consumption. Electric companies can now use historical data to make informed decisions on energy production by forecasting the expected demand. Many deep learning models have been proposed to deal with these types of time series forecasting problems. Deep neural networks, such as recurrent or convolutional, can automatically capture complex patterns in time series data and provide accurate predictions. In particular, Temporal Convolutional Networks (TCN) are a specialised architecture that has advantages over recurrent networks for forecasting tasks. TCNs are able to extract long-term patterns using dilated causal convolutions and residual blocks, and can also be more efficient in terms of computation time. In this work, we propose a TCN-based deep learning model to improve the predictive performance in energy demand forecasting. Two energy-related time series with data from Spain have been studied: the national electric demand and the power demand at charging stations for electric vehicles. An extensive experimental study has been conducted, involving more than 1900 models with different architectures and parametrisations. The TCN proposal outperforms the forecasting accuracy of Long Short-Term Memory (LSTM) recurrent networks, which are considered the state-of-the-art in the field.

scholarly journals Temporal Convolutional Networks Applied to Energy-related Time Series Forecasting

2020 ◽  
Author(s):  
Pedro Lara-Benítez ◽  
Manuel Carranza-García ◽  
José M. Luna-Romera ◽  
José C. Riquelme
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Energy Demand ◽  
Time Series Data ◽  
Demand Forecasting ◽  
Time Series Forecasting ◽  
Series Data ◽  
Recurrent Networks ◽  
Convolutional Networks ◽  
Related Time Series

Modern energy systems collect high volumes of data that can provide valuable information about energy consumption. Electric companies can now use historical data to make informed decisions on energy production by forecasting the expected demand. Many deep learning models have been proposed to deal with these type of time series forecasting problems. Deep neural networks, such as recurrent or convolutional, can automatically capture complex patterns in time series data and provide accurate predictions. In particular, Temporal Convolutional Networks (TCN) are a specialised architecture that has advantages over recurrent networks for forecasting tasks. TCNs are able to extract long-term patterns using dilated causal convolutions and residual blocks, and can also be more efficient in terms of computation time. In this work, we propose a TCN-based deep learning model to improve the predictive performance in energy demand forecasting. Two energy-related time series with data from Spain have been studied: the national electric demand, and the power demand at charging stations for electric vehicles. An extensive experimental study has been conducted, involving more than 1900 models with different architectures and parametrisations. The TCN proposal outperforms the forecasting accuracy of Long Short-Term Memory (LSTM) recurrent networks, which are considered the state-of-the-art in the field.

Deep Learning for text in limted data settings

2020 ◽  
Author(s):  
Pathikkumar Patel ◽  
Bhargav Lad ◽  
Jinan Fiaidhi
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Transfer Learning ◽  
Text Classification ◽  
State Of The Art ◽  
Time Series Forecasting ◽  
Text Data ◽  
Performance Levels

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.

Deep Learning Based Time-Series Forecasting Framework for Olive Precision Farming

2021 ◽  
Author(s):  
Mohammed Atef ◽  
Ahmed Khattab ◽  
Essam A. Agamy ◽  
Mohamed M. Khairy
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Precision Farming ◽  
Time Series Forecasting

scholarly journals Mathematical model for implementing Non Linear Time Series Forecasting using Deep Learning

2020 ◽  
Vol 981 ◽  
pp. 022021
Author(s):  
Imran Qureshi ◽  
Burhanuddin Mohammad ◽  
Mohammed Abdul Habeeb ◽  
Mohammed Ali Shaik
Keyword(s):  
Mathematical Model ◽  
Time Series ◽  
Deep Learning ◽  
Linear Time ◽  
Time Series Forecasting ◽  
Non Linear

Scaling Deep Learning Models for Large Spatial Time-Series Forecasting

2019 ◽  
Author(s):  
Zainab Abbas ◽  
Jon Reginbald Ivarsson ◽  
Ahmad Al-Shishtawy ◽  
Vladimir Vlassov
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Forecasting ◽  
Learning Models

scholarly journals Can C-Reactive Protein (CRP) Time Series Forecasting be Achieved via Deep Learning?

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 59311-59320 ◽  
Author(s):  
Mohsen Dorraki ◽  
Anahita Fouladzadeh ◽  
Stephen J. Salamon ◽  
Andrew Allison ◽  
Brendon J. Coventry ◽  
...  
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Forecasting ◽  
C Reactive Protein ◽  
Reactive Protein
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