Artificial Neural Networks for Time Series Prediction

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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Forecasting with Computational Intelligence - An Evaluation of Support Vector Regression and Artificial Neural Networks for Time Series Prediction

The 2006 IEEE International Joint Conference on Neural Network Proceedings ◽

10.1109/ijcnn.2006.247299 ◽

2006 ◽

Cited By ~ 5

Author(s):

S.F. Crone ◽

S. Lessmann ◽

S. Pietsch

Keyword(s):

Neural Networks ◽

Time Series ◽

Artificial Neural Networks ◽

Support Vector Regression ◽

Computational Intelligence ◽

Time Series Prediction ◽

Support Vector ◽

Artificial Neural

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Time Series Prediction with Artificial Neural Networks: An Analysis Using Brazilian Soybean Production

Agriculture ◽

10.3390/agriculture10100475 ◽

2020 ◽

Vol 10 (10) ◽

pp. 475

Author(s):

Emerson Rodolfo Abraham ◽

João Gilberto Mendes dos Reis ◽

Oduvaldo Vendrametto ◽

Pedro Luiz de Oliveira Costa Neto ◽

Rodrigo Carlo Toloi ◽

...

Keyword(s):

Neural Networks ◽

Time Series ◽

Artificial Neural Networks ◽

Time Series Prediction ◽

Economic Value ◽

Human Consumption ◽

Production Volume ◽

Soybean Production ◽

Production Area ◽

Artificial Neural

Food production to meet human demand has been a challenge to society. Nowadays, one of the main sources of feeding is soybean. Considering agriculture food crops, soybean is sixth by production volume and the fourth by both production area and economic value. The grain can be used directly to human consumption, but it is highly used as a source of protein for animal production that corresponds 75% of the total, or as oil and derived food products. Brazil and the US are the most important players responsible for more than 70% of world production. Therefore, a reliable forecasting is essential for decision-makers to plan adequate policies to this important commodity and to establish the necessary logistical resources. In this sense, this study aims to predict soybean harvest area, yield, and production using Artificial Neural Networks (ANN) and compare with classical methods of Time Series Analysis. To this end, we collected data from a time series (1961–2016) regarding soybean production in Brazil. The results reveal that ANN is the best approach to predict soybean harvest area and production while classical linear function remains more effective to predict soybean yield. Moreover, ANN presents as a reliable model to predict time series and can help the stakeholders to anticipate the world soybean offer.

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Applications of Artificial Neural Networks to Time Series Prediction

Handbook of Neural Network Signal Processing - Electrical Engineering & Applied Signal Processing Series ◽

10.1201/9781420038613.ch9 ◽

2001 ◽

Cited By ~ 1

Author(s):

Yuansong Liao ◽

John Moody ◽

Lizhong Wu

Keyword(s):

Neural Networks ◽

Time Series ◽

Artificial Neural Networks ◽

Time Series Prediction ◽

Artificial Neural

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