Time Series Prediction by Artificial Neural Networks and Differential Evolution in Distributed Environment

2012 ◽  
pp. 198-205 ◽  
Author(s):  
Todor Balabanov ◽  
Iliyan Zankinski ◽  
Nina Dobrinkova
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Differential Evolution ◽  
Time Series Prediction ◽  
Distributed Environment ◽  
Artificial Neural

Artificial Neural Networks for Time Series Prediction

2016 ◽  
pp. 117-150
Author(s):  
Esteban Tlelo-Cuautle ◽  
José de Jesús Rangel-Magdaleno ◽  
Luis Gerardo De la Fraga
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Time Series Prediction ◽  
Artificial Neural

Heterogeneous Space-Time Artificial Neural Networks for Space-Time Series Prediction

2017 ◽  
Vol 22 (1) ◽  
pp. 183-201 ◽  
Author(s):  
Min Deng ◽  
Wentao Yang ◽  
Qiliang Liu ◽  
Rui Jin ◽  
Feng Xu ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Time Series Prediction ◽  
Space Time ◽  
Artificial Neural

scholarly journals Prediction of hierarchical time series using structured regularization and its application to artificial neural networks

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0242099
Author(s):  
Tomokaze Shiratori ◽  
Ken Kobayashi ◽  
Yuichi Takano
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Prediction Model ◽  
Time Series Prediction ◽  
Structured Regularization ◽  
Coherent Forecasts ◽  
Artificial Neural ◽  
Real World Datasets ◽  
Upper Level

This paper discusses the prediction of hierarchical time series, where each upper-level time series is calculated by summing appropriate lower-level time series. Forecasts for such hierarchical time series should be coherent, meaning that the forecast for an upper-level time series equals the sum of forecasts for corresponding lower-level time series. Previous methods for making coherent forecasts consist of two phases: first computing base (incoherent) forecasts and then reconciling those forecasts based on their inherent hierarchical structure. To improve time series predictions, we propose a structured regularization method for completing both phases simultaneously. The proposed method is based on a prediction model for bottom-level time series and uses a structured regularization term to incorporate upper-level forecasts into the prediction model. We also develop a backpropagation algorithm specialized for applying our method to artificial neural networks for time series prediction. Experimental results using synthetic and real-world datasets demonstrate that our method is comparable in terms of prediction accuracy and computational efficiency to other methods for time series prediction.

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