Autoregressive Convolutional Recurrent Neural Network for Univariate and Multivariate Time Series Prediction

Understand

Time Series forecasting (univariate and multivariate) is a problem of high complexity due the different patterns that have to be detected in the input, ranging from high to low frequencies ones.

• In this paper we propose a new model for timeseries prediction that utilizes convolutional layers for feature extraction, a recurrent encoder and a linear autoregressive component.
• We motivate the model and we test and compare it against a baseline of widely used existing architectures for univariate and multivariate timeseries.
• The proposed model appears to outperform the baselines in almost every case of the multivariate timeseries datasets, in some cases even with 50% improvement which shows the strengths of such a hybrid architecture in complex timeseries.