A Hybrid Approach for Time Series Forecasting Using Deep Learning and Nonlinear Autoregressive Neural Networks

During recent decades, several studies have been conducted in the field of weather forecasting providing various promising forecasting models. Nevertheless, the accuracy of the predictions still remains a challenge. In this paper a new forecasting approach is proposed: it implements a deep neural network based on a powerful feature extraction. The model is capable of deducing the irregular structure, non-linear trends and significant representations as features learnt from the data. It is a 6-layered deep architecture with 4 hidden units of Restricted Boltzmann Machine (RBM). The extracts from the last hidden layer are pre-processed, to support the accuracy achieved by the forecaster. The forecaster is a 2-layer ANN model with 35 hidden units for predicting the future intervals. It captures the correlations and regression patterns of the current sample related to the previous terms by using the learnt deep-hierarchal representations of data as an input to the forecaster.