A novel Encoder-Decoder structure for Time Series analysis based on Bayesian Uncertainty reduction

Abstract

In the present work, a novel Convolutional LSTM Encoder-Decoder structure for the implementation of Weather Forecast for the Andean city of Quito is presented. Aside from the above, the Encoder-Decoder structure uses a Walk-Forward validation, an adjustment of the Bayesian posterior predictive distribution and the ADAMW optimizer to carry out the forecast. The aforementioned stages are combined to obtain 4 error metrics per hour. The prediction is done in base of acquired data from a network of Automatic Weather Stations. The results show that the Convolutional Encoder-Decoder structure with a dropout probability of 0.05 and a model precision equal to 0.1 performs better than a LSTM model, LSTM Stacked model or ARIMA models reaching a maximum error of 1.03 °C. Finally, the methodology could be applied as an effective option to implement the post-processing stage for the physical model of a Weather Forecast System.