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Sequence to Sequence Weather Forecasting with Long Short-Term Memory Recurrent Neural Networks
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 143 - Number 11 |
| Year of Publication: 2016 |
| Authors: Mohamed Akram Zaytar, Chaker El Amrani |
10.5120/ijca2016910497
|
Mohamed Akram Zaytar, Chaker El Amrani . Sequence to Sequence Weather Forecasting with Long Short-Term Memory Recurrent Neural Networks. International Journal of Computer Applications. 143, 11 ( Jun 2016), 7-11. DOI=10.5120/ijca2016910497
Abstract
The aim of this paper is to present a deep neural network architecture and use it in time series weather prediction. It uses multi stacked LSTMs to map sequences of weather values of the same length. The final goal is to produce two types of models per city (for 9 cities in Morocco) to forecast 24 and 72 hours worth of weather data (for Temperature, Humidity and Wind Speed). Approximately 15 years (2000-2015) of hourly meteorological data was used to train the model. The results show that LSTM based neural networks are competitive with the traditional methods and can be considered a better alternative to forecast general weather conditions.
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