A Hybrid Deep Neural Network Model for Time Series Forecasting

Deep neural networks have proven to perform optimal forecasts even with the presence of noisyand non-linear nature of time series data. In thispaper, a hybrid deep neural network consisting of Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM) architecture have been proposed. The model combines the convolutional layer's capability of feature extraction along with the LSTM's feature of learning long term sequential dependencies. The experiments were performed on two datasets and compared with four other approaches: Recurrent Neural Network (RNN), LSTM, Gated Recurrent Unit (GRU) and Bidirectional LSTM. All five models are evaluated and compared with one step ahead forecasting. The proposed hybrid CNN-LSTM outperformed other modelsfor both datasets showing robustness against error.